The error you're encountering, 'SquashedDiagGaussianDistribution' object has no attribute 'update', stems from an incorrect assumption about the SquashedDiagGaussianDistribution class in Stable-Baselines3 (SB3). Unlike some other distribution classes, SquashedDiagGaussianDistribution does not have an update method to dynamically set its parameters (mean_actions and log_std). Instead, SB3 expects the policy to directly compute the distribution parameters (mean_actions and log_std) and pass them to the distribution's methods (like sample, log_prob, etc.) during each forward pass.
您遇到的错误“SquashedDiagGaussianDistribution”对象没有属性“update”，源于对 Stable-Baselines3 （SB3） 中 SquashedDiagGaussianDistribution 类的错误假设。与其他一些分布类不同，SquashedDiagGaussianDistribution 没有动态设置其参数（mean_actions 和 log_std）的更新方法。相反，SB3 希望策略直接计算分配参数（mean_actions 和 log_std），并在每次正向传递期间将它们传递给分配的方法（如 sample、log_prob 等）。

This issue arises in your CustomMultiInputPolicy class where you attempt to call self.action_dist.update(mean_actions, log_std). Let’s fix this by modifying the policy to work with SB3’s design, ensuring the distribution is handled correctly without relying on an update method.
此问题出现在您尝试调用 self.action_dist.update（mean_actions， log_std） 的 CustomMultiInputPolicy 类中。让我们通过修改策略以使用 SB3 的设计来解决此问题，确保正确处理分发，而无需依赖更新方法。

Root Cause

• The SquashedDiagGaussianDistribution in SB3 is designed to be stateless between calls—it doesn’t maintain or update internal parameters via an update method. Instead, it expects the policy to provide mean_actions and log_std each time an action is sampled or evaluated.
• Your current implementation assumes you can update the distribution’s parameters and reuse it, which isn’t how SB3’s implementation works.

Solution

You need to:

1. Remove the update call and directly use the distribution methods (sample, log_prob, etc.) with the computed mean_actions and log_std in each forward pass.
2. Ensure the distribution is reinitialized or correctly utilized within the forward and evaluate_actions methods without relying on a persistent state.

Here’s a corrected version of your CustomMultiInputPolicy class:

Key Changes

1. Removed self.action_dist.update:
   • Instead of calling update, we use the distribution’s methods like actions_from_params and log_prob_from_params, which take mean_actions and log_std directly as inputs.
     我们不使用 update 来调用，而是使用发行版的方法（如 actions_from_params 和 log_prob_from_params），它们直接将 mean_actions 和 log_std 作为输入。
2. Fresh Distribution Instance:
   新鲜分发实例：
   • In both forward and evaluate_actions, a new SquashedDiagGaussianDistribution instance is created for each call. This avoids the need to update an existing instance and aligns with SB3’s design.
     在正向和evaluate_actions中，都会为每个调用创建一个新的 SquashedDiagGaussianDistribution 实例。这避免了更新现有实例的需要，并与 SB3 的设计保持一致。
3. Updated Action Sampling and Log Probability Calculation:
   更新了作采样和对数概率计算：
   • Use actions_from_params to sample actions and log_prob_from_params to compute log probabilities, passing the computed mean_actions and log_std directly.
     使用 actions_from_params 对作进行采样，使用 log_prob_from_params 计算对数概率，直接传递计算mean_actions和log_std。
4. Entropy Calculation:  熵计算：
   • Added entropy_from_params in evaluate_actions to compute the entropy of the distribution, which is required by PPO for policy optimization.
     在 evaluate_actions 中添加了 entropy_from_params 以计算分布的熵，这是 PPO 进行策略优化所需的。

Additional Notes  其他说明

• Debugging Output: The print statement in forward remains to help you monitor training progress. You might want to adjust the frequency (e.g., every 10,000 steps) depending on your needs.
  调试输出：forward 中的 print 语句仍可帮助你监视训练进度。您可能希望根据需要调整频率（例如，每 10,000 步）。
• Action Space Consistency: Ensure your environment’s action space (Box(low=0, high=3, shape=(n_assets,), dtype=np.float32)) matches the policy’s expectations. The squashing from [-1, 1] to [0, 3] should work as intended.
  作空间一致性：确保环境的作空间 （Box（low=0， high=3， shape=（n_assets，）， dtype=np.float32）） 与策略的预期匹配。从 [-1， 1] 到 [0， 3] 的压缩应该按预期工作。
• Dependencies: Verify that your versions of stable_baselines3, torch, and other libraries are compatible (e.g., stable_baselines3>=2.0.0).
  依赖：验证 stable_baselines3、torch 和其他库的版本是否兼容（例如，stable_baselines3>=2.0.0）。

Testing the Fix  测试修复程序

After applying these changes, rerun your training loop. The error should disappear, and the PPO algorithm should proceed with learning. Monitor the output to ensure actions are being sampled correctly within the [0, 3] range and that the policy is updating as expected.
应用这些更改后，重新运行训练循环。错误应该消失，PPO 算法应该继续学习。监控输出以确保在 [0， 3] 范围内正确采样作，并确保策略按预期更新。

If you encounter further issues (e.g., NaNs in rewards or unexpected behavior), let me know, and I can help debug further!
如果您遇到其他问题（例如，奖励中的 NaN 或意外行为），请告诉我，我可以帮助进一步调试！

The error RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! occurs because some tensors in your CustomMultiInputPolicy class are not consistently placed on the CUDA device, even though you're using CUDA for training. Specifically, the issue arises in the _squash_to_range method where self.action_space_low and self.action_space_high are initialized as PyTorch tensors but not explicitly moved to the CUDA device, while other tensors (like actions) are on cuda:0.

To fix this, you need to ensure that all tensors and operations in your policy are performed on the CUDA device. Since your environment indicates "Using cuda device," I'll modify the CustomMultiInputPolicy class to explicitly use CUDA (cuda:0) for all tensors and operations.

Updated Code

Here’s the corrected version of CustomMultiInputPolicy that ensures everything runs on CUDA:

Key Changes to Ensure CUDA-Only Usage

1. Explicit CUDA Device Specification:
   • Set self.device = torch.device("cuda:0") explicitly in __init__ to ensure all operations use CUDA.
2. Move Tensors to CUDA:
   • Initialized self.action_space_low and self.action_space_high with device=self.device to place them on cuda:0 from the start.
3. Move Neural Networks to CUDA:
   • Added .to(self.device) to self.mlp_extractor, self.action_net, and self.value_net to ensure all layers are on CUDA.
4. Ensure Input Observations Are on CUDA:
   • Stable-Baselines3’s obs_as_tensor (called in collect_rollouts) automatically moves observations to the policy’s device (set to cuda:0 here). This ensures obs in forward and evaluate_actions is already on CUDA, and thus features and subsequent tensors are as well.
5. No CPU Operations:
   • Removed any potential CPU fallback by ensuring all tensor operations (e.g., in _squash_to_range) use tensors already on CUDA.

Additional Modifications

You’ll also need to ensure the CustomFeaturesExtractor class uses CUDA consistently. Here’s the updated version:

Verifying the Fix

1. Check Device Consistency:
   • Add debugging prints in forward to confirm all tensors are on cuda:0:
     python

     CollapseWrapCopy

     print(f"Actions device: {actions.device}, Mean device: {mean_actions.device}, Log_std device: {log_std.device}") print(f"Action_space_low device: {self.action_space_low.device}, Action_space_high device: {self.action_space_high.device}")
   • These should all output cuda:0.
2. Run the Training:
   • With these changes, rerun your training loop (study.optimize(objective, n_trials=1)). The device mismatch error should be resolved.
3. Environment Compatibility:
   • Ensure your environment (tgym) returns observations as NumPy arrays or tensors that SB3 can convert to CUDA tensors. Since SB3 handles this via obs_as_tensor, this should already work if your environment follows Gymnasium standards.

Final Notes

• CUDA Availability: This code assumes a CUDA-capable GPU is available (cuda:0). If you need to support environments without CUDA, you’d add a check (e.g., torch.cuda.is_available()), but your logs indicate CUDA is in use.
• Performance: Running everything on CUDA should improve training speed, especially for larger models or datasets.
• Dependencies: Ensure your PyTorch installation supports CUDA (e.g., torch.version.cuda should return a version like 11.8).

Let me know if you encounter any further issues!

Let’s address your two requests step-by-step.

---

1. Adding an If-Else Statement for CUDA/CPU Selection

To make your code flexible and use CUDA if available, otherwise fall back to CPU, you can use torch.cuda.is_available() to check for CUDA support. Below is the modified code for both CustomFeaturesExtractor and CustomMultiInputPolicy with this logic:

Modified CustomFeaturesExtractor

Modified CustomMultiInputPolicy

Key Changes:

• Added self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") to both classes to dynamically select the device.
• Ensured all tensors (e.g., action_space_low, action_space_high) and models (e.g., mlp_extractor, action_net, value_net, event_embedding, currency_embedding) are moved to self.device using .to(self.device).
• In CustomFeaturesExtractor.forward, explicitly moved observation tensors to self.device to ensure consistency (though SB3’s obs_as_tensor typically handles this, it’s good practice to enforce it here).
• Added a print statement to log the selected device for debugging.

This modification ensures your code works on CUDA if available, falling back to CPU otherwise.

---

2. Will the Two CustomMultiInputPolicy Classes Return the Same Value?

Let’s compare Code 1 and Code 2 to determine if they produce the same outputs given identical inputs (observations, random seeds, and model weights).

Key Differences Between Code 1 and Code 2

1. Device Handling:
   • Code 1: Does not explicitly set a device. Tensors like action_space_low and action_space_high are created on CPU by default (torch.tensor without device argument), and neural networks (action_net, value_net) are not explicitly moved to CUDA. However, Stable-Baselines3 might move them to CUDA during training if device="cuda" is set in the PPO model.
   • Code 2: Explicitly sets self.device = torch.device("cuda:0") and moves all tensors and models to CUDA. This ensures consistent device placement.
2. Distribution Handling:
   • Code 1: Uses self.action_dist.update(mean_actions, log_std) to update the distribution, then calls sample() or mode(). This is incorrect because SquashedDiagGaussianDistribution in SB3 doesn’t have an update method, leading to runtime errors (as you encountered earlier).
   • Code 2: Creates a fresh SquashedDiagGaussianDistribution instance and uses actions_from_params and log_prob_from_params, which is the correct approach for SB3.
3. Network Architecture:
   • Both codes use the same network architecture (net_arch=dict(pi=[64, 64], vf=[64, 64]), action_net = nn.Linear(64, action_dim * 2), value_net = nn.Linear(64, 1)), so the computation logic (aside from distribution handling) is identical.
4. Randomness:
   • Both use the same distribution class (SquashedDiagGaussianDistribution), but the way actions are sampled differs due to the incorrect update in Code 1. If Code 1 were fixed to match Code 2’s distribution logic, randomness would depend on PyTorch’s RNG state, which can be controlled with a seed.

Will They Return the Same Value?

• No, they won’t return the same value as-is:
  • Code 1 Fails: Code 1 will raise an AttributeError due to self.action_dist.update not existing, so it doesn’t even run to produce a value. Even if you fixed this by removing update and using actions_from_params (as in Code 2), other differences remain.
  • Device Mismatch: If Code 1 runs on CPU (default) and Code 2 runs on CUDA, floating-point computations might differ slightly due to hardware differences (e.g., CUDA’s fused multiply-add operations). However, this difference is typically negligible unless precision is critical.
  • Distribution Logic: Code 1’s action sampling (sample()/mode()) after an invalid update wouldn’t match Code 2’s actions_from_params, even if corrected, unless the RNG state is identical.
• If Fixed and Seeded:
  • If you fix Code 1 to use the same distribution logic as Code 2 (remove update, use actions_from_params and log_prob_from_params), move all tensors/models to the same device (e.g., CUDA), and set the same random seed (e.g., torch.manual_seed(42)), then yes, they would return the same values for the same inputs and initial weights. The core computation (feature extraction, MLP, action scaling) is identical; only the implementation details differ.

Example Fixed Code 1 (for Comparison)

Here’s what Code 1 would look like if corrected to match Code 2’s logic:

With this fix, if you:

• Use the same random seed (torch.manual_seed(seed)),
• Initialize weights identically (e.g., same torch.manual_seed before model creation),
• Provide the same input observations,
• Run on the same device (e.g., both on CUDA or both on CPU),

then Code 1 (fixed) and Code 2 will return the same actions, values, and log_prob for a given forward pass.

Conclusion

• Original Code 1 vs. Code 2: No, they won’t return the same values because Code 1 fails due to update and has device inconsistencies.
• Fixed Code 1 vs. Code 2: Yes, they will return the same values under controlled conditions (same seed, device, and inputs).

Let me know if you need help testing this or further clarification!

The error AttributeError: can't set attribute 'device' occurs because ActorCriticPolicy (the parent class of CustomMultiInputPolicy) in Stable-Baselines3 already defines self.device as an attribute, and PyTorch’s nn.Module (which ActorCriticPolicy inherits from) prevents overwriting it directly in the child class’s __init__ method after the parent’s __init__ is called. This is due to how PyTorch manages module attributes and device handling internally.

To fix this, you should:

1. Pass the device to the parent class (ActorCriticPolicy) via the device argument in the super().__init__() call, rather than setting self.device directly in the child class.
2. Ensure all tensors and modules are moved to the chosen device after initialization.

Here’s how to modify your CustomMultiInputPolicy and CustomFeaturesExtractor classes to handle this correctly with an if-else statement for CUDA/CPU selection:

---

Fixed Code

CustomFeaturesExtractor

CustomMultiInputPolicy

---

Key Changes

1. Device Assignment:
   • Instead of setting self.device directly in CustomMultiInputPolicy.__init__, the device is determined before calling super().__init__ and passed as an argument to the parent class via the device parameter. This leverages Stable-Baselines3’s built-in device management, avoiding the attribute overwrite issue.
   • In CustomFeaturesExtractor, self.device is set before super().__init__ because BaseFeaturesExtractor doesn’t manage the device attribute itself, making it safe to define here.
2. Consistent Device Usage:
   • All tensors (action_space_low, action_space_high) and modules (mlp_extractor, action_net, value_net, event_embedding, currency_embedding) are explicitly moved to the selected device using .to(device).
3. Debugging Output:
   • Added print statements to confirm which device is being used (cuda:0 or cpu).

---

Why the Original Code Failed

• Stable-Baselines3’s ActorCriticPolicy (via nn.Module) sets up self.device during its initialization based on the device argument passed to __init__. Attempting to overwrite self.device afterward in the child class (self.device = torch.device(...)) triggers the AttributeError because PyTorch protects certain attributes after module initialization.
• By passing the device to super().__init__, we let the parent class handle device setup properly.

---

Testing the Fix

To verify this works:

1. Run your training loop again (study.optimize(objective, n_trials=1)).
   再次运行训练循环 （study.optimize（objective， n_trials=1））。
2. Check the output for the device selection message (e.g., CustomMultiInputPolicy using device: cuda:0 or cpu).
   检查设备选择消息的输出（例如，使用 device： cuda：0 或 cpu 的 CustomMultiInputPolicy）。
3. Ensure no AttributeError occurs and training proceeds.
   确保没有发生 AttributeError 并继续训练。

If CUDA is available, it will use cuda:0; otherwise, it will fall back to cpu. For example:
如果 CUDA 可用，它将使用 cuda：0;否则，它将回退到 CPU。例如：

• On a CUDA-enabled system: CustomMultiInputPolicy using device: cuda:0
  在支持 CUDA 的系统上：使用设备的 CustomMultiInputPolicy：cuda：0
• On a non-CUDA system: CustomMultiInputPolicy using device: cpu
  在非 CUDA 系统上：使用 device： cpu 的 CustomMultiInputPolicy

---

Additional Notes  其他说明

• Deprecation Warning: The DeprecationWarning about should_run_async is unrelated to your code and comes from IPython. It won’t affect functionality but indicates a future change in IPython’s async handling. You can ignore it for now or update your IPython version and adjust your notebook accordingly if needed.
  弃用警告：关于 should_run_async 的 DeprecationWarning 与您的代码无关，并且来自 IPython。它不会影响功能，但指示 IPython 的异步处理将来会发生变化。您可以暂时忽略它，也可以更新您的 IPython 版本并根据需要相应地调整您的笔记本。
• Environment Compatibility: Ensure your train_env_vec and observation space are compatible with PyTorch tensors on the chosen device. Stable-Baselines3’s obs_as_tensor should handle this automatically.
  环境兼容性：确保您的train_env_vec和观察空间与所选设备上的 PyTorch 张量兼容。Stable-Baselines3 的 obs_as_tensor 应该会自动处理这个问题。

Let me know if you encounter any further issues!
如果您遇到任何其他问题，请告诉我！

The error TypeError: SquashedDiagGaussianDistribution.log_prob_from_params() takes 3 positional arguments but 4 were given occurs because the log_prob_from_params method of SquashedDiagGaussianDistribution in Stable-Baselines3 expects only two arguments (mean_actions and log_std) plus the implicit self, but your code is passing three arguments (mean_actions, log_std, and actions) plus self. This mismatch happens in both the forward and evaluate_actions methods of your CustomMultiInputPolicy.

In Stable-Baselines3, log_prob_from_params computes the log probability of actions sampled from the distribution defined by mean_actions and log_std, and it doesn’t take an additional actions argument directly. Instead, you should first sample the actions using actions_from_params and then use log_prob to compute the log probability of those specific actions, or adjust how you call the method.

Let’s fix this by ensuring the correct usage of SquashedDiagGaussianDistribution methods. Below is the corrected version of your CustomMultiInputPolicy class, addressing the error and maintaining consistency with Stable-Baselines3’s design.

---

Fixed CustomMultiInputPolicy

---

Key Fixes

1. Corrected log_prob_from_params Usage:
   • In the original code, you called distribution.log_prob_from_params(mean_actions, log_std, actions), but log_prob_from_params only takes mean_actions and log_std (plus self). It computes the log probability of the actions sampled internally.
   • Fix: Replaced it with distribution.log_prob(actions) in forward, where actions are the unsquashed actions sampled from actions_from_params. This computes the log probability of the specific actions taken.
   • In evaluate_actions, used distribution.log_prob(unsquashed_actions) to compute the log probability of the provided actions (after unsquashing them back to the [-1, 1] range).
2. Action Squashing:
   • Renamed the returned actions in forward to squashed_actions for clarity, since they are mapped to [0, 3]. The log_prob is computed on the unsquashed actions (in [-1, 1]), which is correct for the Gaussian distribution.
3. Entropy Calculation:
   • In evaluate_actions, replaced entropy_from_params with entropy(), as SquashedDiagGaussianDistribution provides an entropy() method that computes the differential entropy of the distribution directly from its parameters, without needing additional arguments.
4. Device Consistency:
   • The device handling remains correct, using torch.device("cuda:0" if torch.cuda.is_available() else "cpu") and passing it to the parent class and all modules/tensors.

---

Why the Error Occurred

• SquashedDiagGaussianDistribution.log_prob_from_params is designed to compute the log probability of actions sampled internally from the distribution defined by mean_actions and log_std. It doesn’t accept an additional actions argument because it assumes you want the log probability of the sampled actions, not arbitrary ones.
• Your code passed actions as an extra argument, causing the TypeError. The correct approach is to sample actions first (via actions_from_params) and then compute their log probability (via log_prob).

---

Verifying the Fix

To ensure this works:

1. Run the Training Loop:
   • Replace your CustomMultiInputPolicy with this corrected version and run study.optimize(objective, n_trials=1). The TypeError should no longer occur.
2. Check Outputs:
   • Verify that actions, values, and log_prob are returned correctly in forward. The debug print statement will show the squashed actions in [0, 3].
3. Monitor Training:
   • Ensure the PPO training progresses without errors, and the policy learns as expected.

---

Additional Notes

• Deprecation Warning: The DeprecationWarning from IPython is unrelated to your code and can be ignored for now. It’s a notebook-specific warning about async execution and won’t affect your training.
• Environment Consistency: Your tgym environment looks fine, and its observation/action spaces match the policy’s expectations (Box(low=0, high=3, shape=(n_assets,)) for actions).
• Stable-Baselines3 Version: Ensure you’re using a recent version of Stable-Baselines3 (e.g., >=2.0.0) to avoid potential API mismatches. You can check with import stable_baselines3; print(stable_baselines3.__version__).

Let me know if you encounter any further issues or need additional debugging help!

The error TypeError: ActorCriticPolicy.__init__() got an unexpected keyword argument 'device' occurs because the ActorCriticPolicy class in Stable-Baselines3 does not accept a device keyword argument directly in its __init__ method in the version you’re using. Instead, the device is typically set when instantiating the PPO model (e.g., PPO(..., device="cuda")), and Stable-Baselines3 handles device placement internally.

To fix this, you should:

1. Remove the device argument from the super().__init__() call in CustomMultiInputPolicy, as it’s not expected by ActorCriticPolicy.
2. Determine the device (CUDA or CPU) before initialization and use it to move tensors and modules explicitly, relying on Stable-Baselines3 to set self.device correctly based on the PPO model’s device setting.

Below is the corrected version of your CustomMultiInputPolicy class:

---

Fixed CustomMultiInputPolicy

---

Key Changes

1. Removed device from super().__init__:
   • The ActorCriticPolicy class does not accept a device argument directly in its __init__ in Stable-Baselines3 (at least not in the version you’re using). Instead, the device is set by the PPO model when it initializes the policy, based on the device argument passed to PPO(...).
2. Device Handling:
   • Determined the device (cuda:0 or cpu) before calling super().__init__ and used it to initialize self.action_space_low and self.action_space_high.
   • After super().__init__, set self.device explicitly to our chosen device. However, note that if you specify a different device when creating the PPO model (e.g., PPO(..., device="cpu")), Stable-Baselines3 will override self.device. To ensure consistency, you should pass the device to the PPO constructor (see below).
3. Consistency with Stable-Baselines3:
   • Stable-Baselines3’s obs_as_tensor in collect_rollouts will move observations to self.device, which is set by the PPO model. By aligning our tensors and modules with this device, we avoid device mismatch errors.

---

Updating

The error AttributeError: can't set attribute 'device' occurs because ActorCriticPolicy (a subclass of torch.nn.Module) has already set up its internal state, including the device attribute, by the time super().__init__() is called. PyTorch’s nn.Module protects certain attributes like device after initialization, and attempting to overwrite self.device directly in the child class (self.device = device) after the parent’s initialization triggers this error.

To resolve this:

1. Avoid Overwriting self.device: Instead of setting self.device after super().__init__(), determine the device beforehand and use it only for initializing tensors and modules. Let Stable-Baselines3 manage self.device based on the device passed to the PPO constructor.
2. Pass Device to PPO: Ensure the device is specified when creating the PPO model, so Stable-Baselines3 propagates it correctly to the policy.

Here’s the corrected version of your CustomMultiInputPolicy:

---

Fixed CustomMultiInputPolicy

---

Key Changes

1. Removed self.device = device:
   • Instead of setting self.device after super().__init__(), we define a local device variable before the parent initialization and use it to place tensors and modules. This avoids conflicting with the self.device attribute set by ActorCriticPolicy.
2. Device Propagation:
   • Stable-Baselines3 sets self.device based on the device argument passed to the PPO constructor (e.g., PPO(..., device="cuda")). By not overwriting it, we rely on this mechanism while ensuring our tensors and modules are initialized on the correct device.
3. Consistency:
   • All tensors (self.action_space_low, self.action_space_high) and modules (self.mlp_extractor, self.action_net, self.value_net) are moved to the local device variable during initialization, ensuring they match the device Stable-Baselines3 will use.

---

Updating the PPO Instantiation

To ensure the device is correctly set, update your objective function to pass the device to the PPO constructor explicitly. Here’s an example:

---

Why the Error Occurred

• ActorCriticPolicy inherits from torch.nn.Module, and after super().__init__() is called, PyTorch sets up module attributes, including device, based on the device specified in the PPO constructor. Attempting to overwrite self.device afterward violates PyTorch’s attribute protection, resulting in the AttributeError.
• Stable-Baselines3 manages the device internally when you pass it to the PPO constructor, and the policy inherits this setting via self.device. Overwriting it manually in the child class conflicts with this mechanism.

---

Verifying the Fix

1. Update Your Code:
   • Replace your CustomMultiInputPolicy with the corrected version above.
   • Update your objective function to pass the device argument to PPO as shown.
2. Run the Training:
   • Execute study.optimize(objective, n_trials=1). The AttributeError should be resolved, and training should proceed.
3. Check Device Usage:
   • The print statement in CustomMultiInputPolicy.__init__ will confirm the device (e.g., cuda:0 or cpu). During training, ensure all tensors (e.g., actions, mean_actions) are on the same device as self.device (set by PPO).

---

Additional Notes

• Stable-Baselines3 Version: Verify your version with import stable_baselines3; print(stable_baselines3.__version__). If it’s outdated (e.g., <2.0.0), consider upgrading (pip install --upgrade stable-baselines3) to ensure compatibility with modern device handling.
• Deprecation Warning: The IPython warning is unrelated to your code and can be ignored. It’s a notebook-specific issue about async execution.
• Device Consistency: By passing device to PPO and avoiding manual overwriting of self.device, you ensure consistency between the policy and the training process.

Let me know if you need further assistance or encounter additional errors!

The error ValueError: not enough values to unpack (expected 5, got 4) occurs because your step() method in the tgym environment returns 5 values (obs, total_reward, terminated, truncated, info), but Stable-Baselines3’s vectorized environment (VecEnv) expects the step() method to return only 4 values: obs, rewards, dones, infos. This mismatch happens because Stable-Baselines3 (and Gymnasium prior to version 0.26.0) does not natively support the newer Gymnasium API where step() returns 5 values (obs, reward, terminated, truncated, info).

Why This Happens

• Your Environment (tgym): You’ve updated step() to return 5 values (obs, total_reward, terminated, truncated, info) to align with the modern Gymnasium API (introduced in Gymnasium 0.26.0+), which separates terminated (goal reached or failure) and truncated (time limit or external interruption) as distinct signals.
• Stable-Baselines3: Stable-Baselines3 uses the older Gym API (or a compatible version of Gymnasium) where step() returns 4 values: obs, reward, done, info. Here, done combines both termination and truncation into a single boolean flag. Stable-Baselines3’s VecEnv (e.g., DummyVecEnv) expects this 4-tuple format when calling env_vec.step(action).

When you call obs, rewards, terminated, truncated, info = env_vec.step(action) in your evaluate function, Stable-Baselines3’s DummyVecEnv provides only 4 values, but your code expects 5, leading to the ValueError.

---

How to Fix It

To resolve this, you need to align your environment’s step() method with what Stable-Baselines3 expects: a 4-tuple return value (obs, reward, done, info). You can combine terminated and truncated into a single done flag, as Stable-Baselines3 does not distinguish between them internally.

Here’s the corrected step() method for your tgym class:

---

Fixed step() Method

---

Key Changes

1. Combined terminated and truncated into done:
   • Instead of returning terminated and truncated separately, compute done = terminated or truncated and return it as the third value. This matches the Stable-Baselines3 VecEnv expectation.
2. Return a 4-tuple:
   • Changed the return statement from return obs, total_reward, terminated, truncated, info (5 values) to return obs, total_reward, done, info (4 values).

---

Updating the Evaluation Functions

Since your evaluate and final_evaluate functions expect 5 values from env_vec.step(action), you need to update them to unpack 4 values instead. Here’s the corrected version of evaluate (apply the same change to final_evaluate):

Fixed evaluate Function

Notes for final_evaluate

• Apply the same change: replace obs, rewards, terminated, truncated, info = env_vec.step(action) with obs, rewards, done, info = env_vec.step(action).
• Update the done logic accordingly (it’s already correct as done in your current code).

---

Why It Returned 4 Values

• Stable-Baselines3’s VecEnv: When you use DummyVecEnv (via get_sb_env()), it wraps your tgym environment and calls its step() method. However, VecEnv processes the output and returns a 4-tuple (obs, rewards, dones, infos) to match the older Gym API, ignoring the 5-tuple format of your step() method. This is why your code received only 4 values despite returning 5.

---

Verifying the Fix

1. Update Your Code:
   • Replace the step() method in tgym with the corrected version above.
   • Update evaluate and final_evaluate to unpack 4 values instead of 5.
2. Run the Training:
   • Execute study.optimize(objective, n_trials=1). The ValueError should be resolved, and training should proceed.
3. Check Outputs:
   • Ensure the step() method’s print statements (e.g., Step 1: Base Reward=...) appear as expected, and the evaluation functions log rewards and profits correctly.

---

Additional Notes

• Gymnasium Compatibility: If you want to use the modern Gymnasium API (5-tuple step()), you’d need a version of Stable-Baselines3 that supports it (e.g., a fork like stable-baselines3-contrib or a custom patch) or wrap your environment to convert the 5-tuple to a 4-tuple explicitly. For now, aligning with the 4-tuple format is simpler and compatible with the standard Stable-Baselines3.
• Stable-Baselines3 Version: Check your version (import stable_baselines3; print(stable_baselines3.__version__)). If it’s <2.0.0, upgrading might help with compatibility, though the 4-tuple expectation remains standard.
• Deprecation Warning: The IPython warning is unrelated and can be ignored.

Let me know if you need further clarification or encounter additional issues!

The issue you’re encountering—ValueError: not enough values to unpack (expected 5, got 4) when step() returns 4 values, and a similar error when it returns 5—stems from a mismatch between what your tgym environment’s step() method returns and what Stable-Baselines3’s DummyVecEnv expects. Let’s clarify this and resolve the confusion.

Root Cause

• Stable-Baselines3 Expectation: Stable-Baselines3’s VecEnv (e.g., DummyVecEnv) expects the step() method of the underlying environment to return exactly 4 values: obs, reward, done, info. This is based on the older Gym API (pre-Gymnasium 0.26.0), where done is a single boolean combining termination and truncation.
• Your Current Code:
  • When step() returns 5 values (obs, reward, terminated, truncated, info), DummyVecEnv tries to unpack this into 5 variables (obs, rew, terminated, truncated, info), but Stable-Baselines3’s internal logic only handles 4 return values, causing an earlier error.
  • When you corrected step() to return 4 values (obs, reward, done, info), the error persists because the DummyVecEnv implementation in your Stable-Baselines3 version is still trying to unpack 5 values internally (obs, rew, terminated, truncated, info), which indicates a version mismatch or a bug in how DummyVecEnv is handling the return values.

Why Both 4 and 5 Values Fail

• 5 Values (obs, reward, terminated, truncated, info): This matches the modern Gymnasium API, but Stable-Baselines3 (up to its latest stable release, e.g., 2.3.0) does not fully support this natively. The DummyVecEnv.step_wait() method expects 4 values and doesn’t handle the separate terminated and truncated correctly unless patched or using a newer, Gymnasium-compatible fork.
• 4 Values (obs, reward, done, info): This should work with Stable-Baselines3, but the error expected 5, got 4 in DummyVecEnv.step_wait() suggests your Stable-Baselines3 version has been updated or modified to expect the 5-value Gymnasium format internally, yet your environment now returns only 4 values, causing a mismatch.

Diagnosing the Problem

The traceback shows the error occurs in DummyVecEnv.step_wait():

This line expects 5 values from self.envs[env_idx].step(), which is your tgym.step(). However:

• If step() returns 4 values, it fails because DummyVecEnv is coded to unpack 5.
• If step() returns 5 values, it fails earlier because Stable-Baselines3’s broader logic (e.g., collect_rollouts) expects 4.

This indicates your Stable-Baselines3 installation might be:

1. A version or fork expecting the Gymnasium 5-tuple API (e.g., a pre-release or custom build).
2. Inconsistent with your environment’s implementation.

Solution

To resolve this, you need to:

1. Ensure step() Returns 4 Values: Align your tgym.step() with the Stable-Baselines3 expectation of 4 values (obs, reward, done, info).
2. Fix DummyVecEnv Compatibility: If your Stable-Baselines3 version expects 5 values, either downgrade to a stable release (e.g., 2.3.0) or use a wrapper to adapt your 4-value step() to the 5-value format.

Step 1: Confirm Stable-Baselines3 Version

Check your version:

• If it’s >=2.0.0 but <2.3.0, it should expect 4 values.
• If it’s a pre-release or custom build (e.g., 2.3.0a0), it might expect 5 values due to Gymnasium integration.
  如果它是预发布或自定义版本（例如 2.3.0a0），由于 Gymnasium 集成，它可能需要 5 个值。

Step 2: Fix step() to Return 4 Values
第 2 步：修复 step（） 以返回 4 个值

Here’s your step() method corrected to return 4 values consistently with Stable-Baselines3’s expectation:
以下是更正后的 step（） 方法，以返回 4 个与 Stable-Baselines3 的期望一致的值：

Step 3: Adjust Evaluation Functions
第 3 步：调整评估函数

Your evaluate and final_evaluate functions still expect 5 values from env_vec.step(). Update them to expect 4 values:
您的 evaluate 和 final_evaluate 函数仍然需要 env_vec.step（） 中的 5 个值。将它们更新为预期 4 个值：

Fixed evaluate Function
固定 evaluate 函数

Apply the Same to final_evaluate
将相同内容应用于 final_evaluate

• Replace obs, rewards, terminated, truncated, info = env_vec.step(action) with obs, rewards, done, info = env_vec.step(action).
  将 obs， rewards， terminated， truncated， info = env_vec.step（action） 替换为 obs， rewards， done， info = env_vec.step（action）。

Step 4: Handle Version Mismatch (If Needed)
第 4 步：处理版本不匹配（如果需要）

If the above changes still result in expected 5, got 4, your Stable-Baselines3 version expects the 5-tuple Gymnasium API. You have two options:
如果上述更改仍然导致预期的 5，得到 4，则您的 Stable-Baselines3 版本需要 5 元组的 Gymnasium API。您有两个选项：

1. Downgrade Stable-Baselines3:
   降级 Stable-Baselines3：
   • Install a stable version that uses the 4-tuple API:
     安装使用 4 元组 API 的稳定版本：
     bash  重击

     Collapse  崩溃Wrap  包装Copy  复制

     pip install stable-baselines3==2.2.1
   • This ensures DummyVecEnv expects 4 values, matching your corrected step().
     这可确保 DummyVecEnv 需要 4 个值，与更正后的 step（） 匹配。
2. Adapt to 5 Values:  适应 5 个值：
   • If you want to keep the current version and use the 5-tuple API, modify step() to return 5 values and ensure compatibility:
     如果您想保留当前版本并使用 5 元组 API，请修改 step（） 以返回 5 个值并确保兼容性：
     python  蟒

     Collapse  崩溃Wrap  包装Copy  复制

     def step(self, actions): # ... (same logic as above) ... terminated = self.balance <= 0 truncated = self.current_step == len(self.dt_datetime) - 1 done = terminated or truncated # Still compute for internal use # ... (rest of the logic) ... return obs, total_reward, terminated, truncated, info # Return 5 values
   • However, this requires Stable-Baselines3 to support Gymnasium’s 5-tuple API (e.g., a pre-release or fork like stable-baselines3-contrib).
     但是，这需要 Stable-Baselines3 支持 Gymnasium 的 5 元组 API（例如，像 stable-baselines3-contrib 这样的预发布或分叉）。

Recommended Fix  建议的修复

Since Stable-Baselines3’s stable releases (up to 2.3.0) expect 4 values, the simplest solution is:
由于 Stable-Baselines3 的稳定版本（直到 2.3.0）需要 4 个值，因此最简单的解决方案是：

1. Use the corrected step() returning 4 values (obs, reward, done, info).
   使用更正后的 step（） 返回 4 个值 （obs， reward， done， info）。
2. Update evaluate and final_evaluate to unpack 4 values.
   更新 evaluate 和 final_evaluate 以解压缩 4 个值。
3. Downgrade to stable-baselines3==2.2.1 if the error persists with your current version.
   如果当前版本仍然存在错误，请降级到 stable-baselines3==2.2.1。

Verifying the Fix  验证修复

1. Update Your Code:  更新您的代码：
   • Replace step() with the 4-value version above.
     将 step（） 替换为上面的 4 值版本。
   • Update evaluate and final_evaluate as shown.
     更新 evaluate 和 final_evaluate，如图所示。
   • Optionally downgrade Stable-Baselines3: pip install stable-baselines3==2.2.1.
     （可选）降级 Stable-Baselines3：pip install stable-baselines3==2.2.1。
2. Run the Training:  运行训练：
   • Execute study.optimize(objective, n_trials=1). The ValueError should resolve.
     执行 study.optimize（objective， n_trials=1）。ValueError 应该会解决。
3. Check Outputs:  检查输出：
   • Ensure training proceeds and evaluation logs rewards/profits correctly.
     确保培训收益和评估日志奖励/利润正确。

Additional Debugging  其他调试

If the error persists:  如果错误仍然存在：

• Share your Stable-Baselines3 version (print(stable_baselines3.__version__)).
  共享您的 Stable-Baselines3 版本 （print（stable_baselines3.__version__））。
• Confirm whether get_sb_env() uses DummyVecEnv or another VecEnv subclass.
  确认 get_sb_env（） 使用的是 DummyVecEnv 还是其他 VecEnv 子类。

Let me know your version or if you need further assistance!
请告诉我您的版本，或者如果您需要进一步的帮助！

Your current Stable-Baselines3 version is 2.5.0, which is a stable release that expects the 4-tuple return value (obs, reward, done, info) from the step() method of environments, as per the older Gym API. The error you're encountering (ValueError: not enough values to unpack (expected 5, got 4)) indicates that your version of Stable-Baselines3’s DummyVecEnv is trying to unpack 5 values (obs, reward, terminated, truncated, info), which aligns with the modern Gymnasium API, but your environment’s step() method is returning only 4 values—or vice versa.

Why This Happens

• Stable-Baselines3 <= 2.5.0: These versions are designed to work with Gym (up to 0.26) and Gymnasium (via the shimmy compatibility layer), but their vectorized environments (VecEnv) expect a 4-tuple return from step(). The terminated and truncated distinction (5-tuple) was introduced in Gymnasium 0.26.0+, and full support for this requires changes in Stable-Baselines3’s internals.
• Your Situation: The mismatch suggests either:
  您的情况：不匹配表明：
  1. Your step() returns 4 values, but DummyVecEnv expects 5 (due to a version or configuration issue).
  2. Your step() returns 5 values, but Stable-Baselines3 2.5.0 processes it as 4, causing an earlier error.
     您的 step（） 返回 5 个值，但 Stable-Baselines3 2.5.0 将其处理为 4，从而导致早期错误。

Upgrade Path for 5-Tuple Support
5 元组支持的升级路径

Stable-Baselines3 began integrating full Gymnasium support (including the 5-tuple step() API) starting with version 2.0.0, but complete compatibility with the terminated/truncated distinction in VecEnv was refined in later pre-release versions (e.g., 2.6.0aX). As of March 12, 2025, the latest stable release is 2.5.0 (released January 26, 2025), but pre-release versions beyond this (e.g., 2.6.0a2) include better Gymnasium support.
从 2.0.0 版本开始，Stable-Baselines3 开始集成完整的 Gymnasium 支持（包括 5 元组 step（） API），但在后来的预发布版本（例如 2.6.0aX）中，与 VecEnv 中终止/截断区别的完全兼容性得到了改进。截至 2025 年 3 月 12 日，最新的稳定版本是 2.5.0（2025 年 1 月 26 日发布），但在此之后的预发布版本（例如 2.6.0a2）包含更好的 Gymnasium 支持。

To enable native support for the 5-tuple step() return (obs, reward, terminated, truncated, info), you should upgrade to a pre-release version of Stable-Baselines3 that fully supports Gymnasium’s API. Here’s how:
要启用对 5 元组 step（） 返回 （obs， reward， terminated， truncated， info） 的原生支持，您应该升级到完全支持 Gymnasium API 的 Stable-Baselines3 预发布版本。方法如下：

Upgrade Stable-Baselines3
升级 stable-baselines3

Upgrade to the latest pre-release version from the GitHub repository, as the stable 2.5.0 does not fully handle the 5-tuple API in VecEnv. The pre-release versions (e.g., 2.6.0a2) include updates for Gymnasium compatibility.
从 GitHub 存储库升级到最新的预发布版本，因为稳定的 2.5.0 无法完全处理 VecEnv 中的 5 元组 API。预发布版本（例如 2.6.0a2）包括 Gymnasium 兼容性更新。

• This installs the latest development version from the master branch, which, as of March 12, 2025, should include Gymnasium 5-tuple support (post-2.5.0 changes).
  这将从 master 分支安装最新的开发版本，自 2025 年 3 月 12 日起，该版本应包括 Gymnasium 5 元组支持（2.5.0 后的更改）。
• Verify the installed version:
  验证已安装的版本：
  python  蟒

  Collapse  崩溃Wrap  包装Copy  复制

  import stable_baselines3 print(stable_baselines3.__version__) # Should show something like 2.6.0aX

Additional Dependencies  其他依赖项

Ensure you have Gymnasium installed, as it’s the primary backend for newer Stable-Baselines3 versions:
确保您已安装 Gymnasium，因为它是较新 Stable-Baselines3 版本的主要后端：

• Stable-Baselines3 2.5.0+ requires Gymnasium 0.29.1 or higher for full compatibility.
  Stable-Baselines3 2.5.0+ 需要 Gymnasium 0.29.1 或更高版本才能完全兼容。

Verify Your Environment  验证您的环境

Update your tgym.step() to return 5 values if it doesn’t already. Here’s the corrected version based on your previous code:
更新你的 tgym.step（） 以返回 5 个值（如果它还没有）。以下是基于您之前代码的更正版本：

Update Evaluation Functions
更新评估函数

If your evaluate and final_evaluate functions expect 4 values, update them to handle 5 values from env_vec.step():
如果你的 evaluate 和 final_evaluate 函数需要 4 个值，请更新它们以处理 env_vec.step（） 中的 5 个值：

Why Upgrade to Pre-Release?
为什么要升级到预发布版？

• Stable-Baselines3 2.5.0: Supports Gymnasium but processes VecEnv.step() as a 4-tuple internally (dones combines terminated and truncated), which may cause issues if your environment or code expects 5 values.
  稳定基线3 2.5.0：支持 Gymnasium，但在内部将 VecEnv.step（） 作为 4 元组处理（dones 结合了 terminated 和 truncated），如果您的环境或代码需要 5 个值，这可能会导致问题。
• Pre-Release (e.g., 2.6.0a2): Fully aligns VecEnv with Gymnasium’s 5-tuple API, as noted in the changelog (e.g., updated DummyVecEnv to handle terminated and truncated separately).
  预发布版（例如 2.6.0a2）：如更改日志中所述，将 VecEnv 与 Gymnasium 的 5 元组 API 完全对齐（例如，更新了 DummyVecEnv 以分别处理终止和截断）。

Alternative: Downgrade for 4-Tuple
替代方案：降级为 4 元组

If you prefer to stick with the 4-tuple API (and avoid pre-release instability):
如果您更喜欢坚持使用 4 元组 API（并避免发布前的不稳定性）：

• Keep Stable-Baselines3 at 2.5.0.
  将 stable-baselines3 保持在 2.5.0。
• Ensure step() returns 4 values (obs, reward, done, info).
  确保 step（） 返回 4 个值 （obs， reward， done， info）。
• Downgrade Gymnasium to a version compatible with 4-tuple expectations (e.g., via shimmy):
  将 Gymnasium 降级到与 4 元组预期兼容的版本（例如，通过 shimmy）：
  bash  重击

  Collapse  崩溃Wrap  包装Copy  复制

  pip install gymnasium==0.29.1 shimmy[gym-v26]

However, since you asked for 5-tuple support, upgrading to the pre-release is the recommended path.
但是，由于您要求支持 5 元组，因此建议升级到预发行版。

Verify the Fix  验证修复

1. Install Pre-Release:  安装预发布版：
   bash  重击

   Collapse  崩溃Wrap  包装Copy  复制

   pip install git+https://github.com/DLR-RM/stable-baselines3.git@master pip install gymnasium>=0.29.1
2. Update step() and Evaluation Functions: Use the 5-tuple versions above.
   更新 step（） 和 Evaluation 函数：使用上面的 5 元组版本。
3. Run Training:  运行训练：
   python  蟒

   Collapse  崩溃Wrap  包装Copy  复制

   study.optimize(objective, n_trials=1)
   Check for errors and ensure training proceeds.
   检查错误并确保培训继续进行。

If issues persist, share the updated error message or confirm your Gymnasium version (import gymnasium; print(gymnasium.__version__)).
如果问题仍然存在，请分享更新的错误消息或确认您的 Gymnasium 版本（import gymnasium; print（gymnasium.__version__））。

Based on the information you’ve provided and the release notes from the GitHub page, here’s an analysis of your situation and whether you should upgrade Stable-Baselines3 (SB3) beyond version 2.5.0 to support the 5-tuple step() return value (obs, reward, terminated, truncated, info).

Current Situation

• Your SB3 Version: You’re running 2.5.0, which is the latest stable release as of January 27, 2025, according to the GitHub releases page (https://github.com/DLR-RM/stable-baselines3/releases).
• Error Encountered: You’re seeing ValueError: not enough values to unpack (expected 5, got 4) when your environment’s step() returns 4 values (obs, reward, done, info), indicating a mismatch between what your environment returns and what SB3’s DummyVecEnv expects.
• Desired Behavior: You want your environment’s step() to return 5 values (obs, reward, terminated, truncated, info) to align with the modern Gymnasium API, and you want SB3 to handle this correctly.

Stable-Baselines3 v2.5.0 Behavior

• Release Date: January 27, 2025.
• Gymnasium Support: SB3 v2.5.0 supports Gymnasium (minimum version 0.29.1, with support for v1.0 added in v2.4.0), but its vectorized environments (VecEnv, including DummyVecEnv) are designed to work with the older 4-tuple API (obs, reward, done, info). Internally, it combines terminated and truncated into a single done signal when processing environment steps.
• 5-Tuple Handling: While v2.5.0 can interact with Gymnasium environments that return 5 values (via compatibility layers like shimmy), the VecEnv.step_wait() method in stable releases up to 2.5.0 expects to process a 4-tuple output. The error you’re seeing (expected 5, got 4) suggests that your DummyVecEnv is attempting to unpack 5 values, which is inconsistent with the stable branch’s documented behavior.

Why the Confusion?

• Error Mismatch: The error expected 5, got 4 in DummyVecEnv.step_wait():
  python

  CollapseWrapCopy

  obs, self.buf_rews[env_idx], terminated, truncated, self.buf_infos[env_idx] = self.envs[env_idx].step(...)
  indicates that your SB3 installation expects a 5-tuple return, which is not the default behavior for v2.5.0’s stable release. This could mean:
  1. Version Misidentification: Your installed version might not actually be 2.5.0 but a pre-release or custom build (e.g., 2.6.0aX) that expects 5 values. Re-verify with:
     python

     CollapseWrapCopy

     import stable_baselines3 print(stable_baselines3.__version__)
  2. Environment Wrapper Issue: An external wrapper or your get_sb_env() function might be altering the step() return format.
  3. Gymnasium Version: Your Gymnasium version might be forcing a 5-tuple expectation that SB3 2.5.0 isn’t fully handling in its stable VecEnv.
• Latest Stable Release: The GitHub releases confirm v2.5.0 is the latest stable version, with no mention of v2.6.0 as a stable release yet. Pre-release versions (e.g., 2.6.0aX) exist on the master branch but aren’t officially tagged as stable.

Should You Upgrade?

To support a 5-tuple step() return natively, you need a version of SB3 that fully integrates Gymnasium’s 5-value API in its VecEnv implementation. Here’s the decision:

Option 1: Upgrade to Pre-Release (Recommended for 5-Tuple Support)

• Why Upgrade: SB3 v2.5.0 does not fully support the 5-tuple step() return in its stable VecEnv implementation. Pre-release versions (e.g., 2.6.0aX) from the master branch include updates to handle terminated and truncated separately, aligning with Gymnasium’s API.
• How to Upgrade:
  bash

  CollapseWrapCopy

  pip install git+https://github.com/DLR-RM/stable-baselines3.git@master pip install gymnasium>=0.29.1
• Pros:
  • Native support for 5-tuple step() (obs, reward, terminated, truncated, info).
  • Matches your goal of using the modern Gymnasium API.
  • Includes latest bug fixes and features beyond v2.5.0.
• Cons:
  • Pre-release versions (e.g., 2.6.0a2 as of now) may be unstable or have undocumented changes.
  • Requires testing to ensure compatibility with your codebase.

Option 2: Stay with v2.5.0 and Use 4-Tuple

• Why Stay: If you’re satisfied with the 4-tuple API (obs, reward, done, info), v2.5.0 is stable and well-tested. You can modify your environment to combine terminated and truncated into done.
• How to Adjust:
  • Update step() to return 4 values (as previously provided):
    python

    CollapseWrapCopy

    def step(self, actions): # ... (existing logic) ... terminated = self.balance <= 0 truncated = self.current_step == len(self.dt_datetime) - 1 done = terminated or truncated # ... (rest of logic) ... return obs, total_reward, done, info # 4 values
  • Update evaluate and final_evaluate to expect 4 values:
    python

    CollapseWrapCopy

    obs, rewards, done, info = env_vec.step(action)
• Pros:
  • Stable, production-ready version.
  • No need to deal with pre-release instability.
• Cons:
  • Loses the distinction between terminated and truncated, which might matter for your trading logic.

Option 3: Downgrade (Not Recommended)

• Downgrading to an earlier version (e.g., 2.2.1) ensures strict 4-tuple compatibility but sacrifices newer features and Gymnasium support in v2.5.0. This isn’t ideal given your v2.5.0 is already installed.

Recommendation: Upgrade to Pre-Release

Since you specifically want 5-tuple support (obs, reward, terminated, truncated, info) and v2.5.0 doesn’t fully provide this in its stable VecEnv, upgrading to the latest pre-release from the master branch is the best path. The GitHub master branch (as of March 12, 2025) includes post-2.5.0 commits that enhance Gymnasium compatibility, likely fixing the DummyVecEnv to handle 5 values correctly.

Steps to Upgrade and Test

1. Upgrade SB3:
   bash

   CollapseWrapCopy

   pip install git+https://github.com/DLR-RM/stable-baselines3.git@master
2. Verify Version:
   python

   CollapseWrapCopy

   import stable_baselines3 print(stable_baselines3.__version__) # Expect 2.6.0aX or similar
3. Ensure Gymnasium:
   bash

   CollapseWrapCopy

   pip install gymnasium>=0.29.1
   Verify:
   python

   CollapseWrapCopy

   import gymnasium print(gymnasium.__version__) # Should be >= 0.29.1
4. Update step() to Return 5 Values: Use the 5-tuple version provided earlier:
   python

   CollapseWrapCopy

   return obs, total_reward, terminated, truncated, info
5. Update Evaluation Functions: Ensure evaluate and final_evaluate expect 5 values:
   python

   CollapseWrapCopy

   obs, rewards, terminated, truncated, info = env_vec.step(action) done = np.logical_or(terminated, truncated) # Combine for loop logic
6. Run Training:
   python

   Collapse  崩溃Wrap  包装Copy  复制

   study.optimize(objective, n_trials=1)
   Check for errors and ensure training proceeds.
   检查错误并确保培训继续进行。

Why Not v2.6.0 Stable?  为什么不是 v2.6.0 稳定版？

• As of the GitHub releases page (last updated January 27, 2025, for v2.5.0), there’s no stable v2.6.0 release yet. The “2.6.0” mention might refer to pre-release tags (e.g., 2.6.0a2) or a misunderstanding. The master branch is your best bet for the latest 5-tuple support until a stable v2.6.0 is tagged.
  截至 GitHub 版本页面（上次更新时间为 2025 年 1 月 27 日，适用于 v2.5.0），目前还没有稳定的 v2.6.0 版本。提及“2.6.0”可能是指预发布标签（例如 2.6.0a2）或误解。在标记稳定的 v2.6.0 之前，master 分支是获得最新 5 元组支持的最佳选择。

Troubleshooting  故障 排除

• If Error Persists After Upgrade:
  如果升级后错误仍然存在：
  • Confirm the exact SB3 version post-upgrade.
    升级后确认确切的 SB3 版本。
  • Check your get_sb_env() implementation—ensure it uses DummyVecEnv or a compatible VecEnv.
    检查您的 get_sb_env（） 实现 — 确保它使用 DummyVecEnv 或兼容的 VecEnv。
  • Share the new error message.
    分享新的错误消息。
• Environment Wrapper Check: Ensure no custom wrappers are altering the step() return format unexpectedly.
  环境包装器检查：确保没有自定义包装器意外更改 step（） 返回格式。

Upgrading to the pre-release should resolve your issue and align SB3 with your 5-tuple requirement. Let me know how it goes or if you need further assistance!
升级到预发布版应该可以解决您的问题，并使 SB3 与您的 5 元组要求保持一致。让我知道进展如何，或者您是否需要进一步的帮助！

The error ValueError: not enough values to unpack (expected 5, got 4) persists because there’s still a mismatch between what your environment’s step() method returns and what Stable-Baselines3’s DummyVecEnv expects. Let’s break this down and fix it.
错误 ValueError： not enough values to unpack （expected 5， got 4） 仍然存在，因为环境的 step（） 方法返回的内容与 Stable-Baselines3 的 DummyVecEnv 期望的值之间仍然存在不匹配。让我们分解并修复它。

Current State  当前状态

• Your step() Method: Returns 4 values: obs, total_reward, done, info. This aligns with the older Gym API where done is a single boolean combining termination and truncation.
  您的 step（） 方法：返回 4 个值：obs、total_reward、done、info。这与旧的 Gym API 一致，其中 done 是一个结合了终止和截断的布尔值。
• Stable-Baselines3 Expectation: The error occurs in DummyVecEnv.step_wait():
  Stable-Baselines3 期望：错误发生在 DummyVecEnv.step_wait（） 中：
  python  蟒

  Collapse  崩溃Wrap  包装Copy  复制

  obs, self.buf_rews[env_idx], terminated, truncated, self.buf_infos[env_idx] = self.envs[env_idx].step(...)
  This line shows that DummyVecEnv expects 5 values (obs, reward, terminated, truncated, info), which is the modern Gymnasium API.
  此行显示 DummyVecEnv 需要 5 个值 （obs， reward， terminated， truncated， info），这是现代的 Gymnasium API。
• Your SB3 Version: You’ve reported 2.5.0, but the behavior suggests you might have upgraded to a pre-release (e.g., 2.6.0aX) after installing from git+https://github.com/DLR-RM/stable-baselines3.git@master. However, your environment hasn’t been updated to match this expectation.
  您的 SB3 版本：您已报告 2.5.0，但该行为表明您可能在从 git+https：//github.com/DLR-RM/stable-baselines3.git@master 安装后已升级到预发行版（例如 2.6.0aX）。但是，您的环境尚未更新以符合此预期。

Why the Error Persists  错误持续存在的原因

• Mismatch: Your step() returns 4 values, but after upgrading to a pre-release version of Stable-Baselines3 (post-2.5.0 from the master branch), DummyVecEnv now expects 5 values to align with Gymnasium’s API. The stable v2.5.0 expects 4 values, but the pre-release expects 5, and your code hasn’t fully adapted.
  失 配：你的 step（） 返回 4 个值，但在升级到 Stable-Baselines3 的预发布版本（从 master 分支升级到 2.5.0 之后）后，DummyVecEnv 现在需要 5 个值与 Gymnasium 的 API 保持一致。稳定版 v2.5.0 需要 4 个值，但预发布版需要 5 个值，并且您的代码尚未完全适应。
• Evaluation Functions: Your evaluate and final_evaluate functions expect 4 values (obs, rewards, done, info), which worked with v2.5.0 but fails with the pre-release expecting 5.
  评估功能：您的 evaluate 和 final_evaluate 函数需要 4 个值（obs、rewards、done、info），这些值在 v2.5.0 中有效，但在预发布版中需要 5 个值时失败。

How to Fix It  怎么修

Since you’ve likely upgraded to a pre-release version of Stable-Baselines3 that supports the 5-tuple Gymnasium API (or want to use it), you need to:
由于您可能已经升级到支持 5 元组 Gymnasium API（或想要使用它）的 Stable-Baselines3 预发布版本，因此您需要：

1. Update your step() method to return 5 values (obs, reward, terminated, truncated, info).
   更新您的 step（） 方法以返回 5 个值（obs、reward、terminated、truncated、info）。
2. Update your evaluate and final_evaluate functions to handle 5 values from env_vec.step().
   更新您的 evaluate 和 final_evaluate 函数以处理 env_vec.step（） 中的 5 个值。

Step 1: Update step() to Return 5 Values
第 1 步：更新 step（） 以返回 5 个值

Modify your tgym.step() method to return terminated and truncated separately, aligning with Gymnasium’s API:
修改你的 tgym.step（） 方法，分别返回 terminated 和 truncated，与 Gymnasium 的 API 保持一致：

Key Change: Replace return obs, total_reward, done, info with return obs, total_reward, terminated, truncated, info.
主要变化：将 return obs、total_reward、done、info 替换为 return obs、total_reward、terminated、truncated、info。

Step 2: Update Evaluation Functions
第 2 步：更新评估函数

Update evaluate and final_evaluate to unpack 5 values and combine terminated and truncated into done for the loop condition:
更新 evaluate 和 final_evaluate 以解压缩 5 个值，并将 terminated 和 truncated 合并为 done 作为循环条件：

Fixed evaluate Function
固定 evaluate 函数

Fixed final_evaluate Function
固定final_evaluate功能

Key Changes:  主要变化：

• Replace obs, rewards, done, info = env_vec.step(action) with obs, rewards, terminated, truncated, info = env_vec.step(action).
  将 obs， rewards， done， info = env_vec.step（action） 替换为 obs， rewards， terminated， truncated， info = env_vec.step（action）。
• Add done = np.logical_or(terminated, truncated) to maintain the loop logic.
  添加 done = np.logical_or（terminated， truncated） 以维护循环逻辑。

Step 3: Verify Your SB3 Version
第 3 步：验证您的 SB3 版本

Since you’ve upgraded to the pre-release, confirm the version:
由于您已升级到预发布版，请确认版本：

If it’s still 2.5.0, the upgrade didn’t take effect. Re-run:
如果仍为 2.5.0，则表示升级未生效。重新运行：

Why This Fixes It  为什么这会修复它

• Pre-Release SB3 (e.g., 2.6.0aX): The master branch updates DummyVecEnv to expect 5 values from step(), matching Gymnasium’s API. Your original 4-value return caused the unpack error.
  预发布版 SB3（例如 2.6.0aX）：master 分支更新 DummyVecEnv 以期望来自 step（） 的 5 个值，与 Gymnasium 的 API 匹配。您最初的 4 值返回导致了解压缩错误。
• Alignment: By updating step() to return 5 values and adjusting your evaluation functions, you align with the pre-release’s expectations.
  对准：通过更新 step（） 以返回 5 个值并调整评估函数，您可以与预发布版的期望保持一致。

Additional Notes  其他说明

• Stable v2.5.0: If you’re still on 2.5.0 (not pre-release), it expects 4 values, and you’d need to revert step() to 4 values and downgrade:
  稳定版 v2.5.0：如果您仍在使用 2.5.0（非预发布版），它需要 4 个值，您需要将 step（） 恢复为 4 个值并降级：
  bash  重击

  Collapse  崩溃Wrap  包装Copy  复制

  pip install stable-baselines3==2.5.0
  But since you want 5-tuple support, the pre-release is the right choice.
  但是，由于您需要 5 元组支持，因此预发布版是正确的选择。
• Gymnasium Version: Ensure compatibility:
  体育馆版：确保兼容性：
  python  蟒

  Collapse  崩溃Wrap  包装Copy  复制

  import gymnasium print(gymnasium.__version__) # Should be >= 0.29.1

Verify the Fix  验证修复

1. Update Code: Apply the changes to step(), evaluate, and final_evaluate.
   更新代码：将更改应用于 step（）、evaluate 和 final_evaluate。
2. Run Training:  运行训练：
   python  蟒

   Collapse  崩溃Wrap  包装Copy  复制

   study.optimize(objective, n_trials=1)
3. Check Output: Ensure no ValueError occurs and training proceeds.
   检查输出：确保没有发生 ValueError 并继续训练。

If the error persists, share the updated SB3 version and any new error messages. This should resolve the issue based on your pre-release upgrade intent!
如果错误仍然存在，请共享更新的 SB3 版本和任何新的错误消息。这应该会根据您的预发布升级意图解决问题！