Answers.Body: <p>PPO's clipping mechanism addresses policy update stability rather than action constraints and the <code>tanh</code> transformed sampled actions are already constrained in the range of [-1, 1], so there's no need to clip the actions further as long as your transformation is applied consistently and log-probabilities of the actions are adjusted to account for the <code>tanh</code> transformation during training to ensure valid policy gradient updates, possibly via said transformation's Jocobian determinant.</p>
  <p>Your first approach can distort the policy gradient as the gradient of the clipped action doesn't correspond to the sampled action at all and there's no way you can adjust to account for such non-differentiable clipping transformation as it's essentially loss of information. This can lead to suboptimal training.</p>
  <p>Therefore your third approach is the best comparatively speaking.</p>
  
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Answers.Text: Hi, thanks for your response! I understand the third approach is the most suitable. However, I'm concerned that using tanh might limit exploration because the std could be constrained too much, leading to actions clustering near the boundaries of [-1, 1].
  
  Do you think this could be a problem, and if so, do you have any suggestions to improve exploration, like adjusting the std range or using a different transformation?
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Answers.Text: It's a valid concern, clip the learned std's for all states to a reasonable range to ensure learned Gaussian distributions not too wide since learning a wide std is useless anyway. Also you can add noise to the means to encourage exploration which is limited by the possible clustering near the boundaries of [-1,1]. And if your action constraints are not violated very often by untransformed raw Gaussian sampled actions from learned parameters for all states during PPO training, then you can also try 2nd method which is fine under such rare clip assumption. Try both and compare as this is an art
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Body: <p>I'm working on a reinforcement learning task using PPO with a continuous action space, and I'm using a Gaussian distribution to model the action outputs. My concern is how to properly constrain the generated actions within the action space limits, particularly when using Gaussian distributions.</p>
  <p>I have come up with three potential solutions and would like some advice on which one is the most reasonable:</p>
  <p>Directly output mean and std from the network, sample actions from the distribution, then clip the actions to ensure they remain within the specified action space.</p>
  <p>Output tanh processed mean and std from the network, sample actions, and then clip the actions if needed. This approach usually ensures that most actions are within the -1 to 1 range, but some might exceed this range due to the standard deviation, so clipping is still necessary.</p>
  <p>Directly output mean and std, sample actions from the distribution, and then apply tanh to the action before returning it. This ensures that the action is always within the -1 to 1 range without the need for clipping.</p>
  <p>Which of these approaches is the most suitable for constraining actions in a continuous action space using Gaussian distributions in PPO? Are there any drawbacks or better alternatives I should consider?</p>
  
Tags: <reinforcement-learning><proximal-policy-optimization>
Title: How to constrain actions in a continuous action space with Gaussian distribution in PPO?
UserDisplayName: Weitao Kang
ThreadText: # How to constrain actions in a continuous action space with Gaussian distribution in PPO?
  
  (Asked by: username_0 on 2024-12-02)
  
  I'm working on a reinforcement learning task using PPO with a continuous action space, and I'm using a Gaussian distribution to model the action outputs. My concern is how to properly constrain the generated actions within the action space limits, particularly when using Gaussian distributions.
  
  I have come up with three potential solutions and would like some advice on which one is the most reasonable:
  
  Directly output mean and std from the network, sample actions from the distribution, then clip the actions to ensure they remain within the specified action space.
  
  Output tanh processed mean and std from the network, sample actions, and then clip the actions if needed. This approach usually ensures that most actions are within the -1 to 1 range, but some might exceed this range due to the standard deviation, so clipping is still necessary.
  
  Directly output mean and std, sample actions from the distribution, and then apply tanh to the action before returning it. This ensures that the action is always within the -1 to 1 range without the need for clipping.
  
  Which of these approaches is the most suitable for constraining actions in a continuous action space using Gaussian distributions in PPO? Are there any drawbacks or better alternatives I should consider?
  
  **Tags:** reinforcement-learning, proximal-policy-optimization
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-02. Score: 0
  
  PPO's clipping mechanism addresses policy update stability rather than action constraints and the `tanh` transformed sampled actions are already constrained in the range of [-1, 1], so there's no need to clip the actions further as long as your transformation is applied consistently and log-probabilities of the actions are adjusted to account for the `tanh` transformation during training to ensure valid policy gradient updates, possibly via said transformation's Jocobian determinant.
  
  Your first approach can distort the policy gradient as the gradient of the clipped action doesn't correspond to the sampled action at all and there's no way you can adjust to account for such non-differentiable clipping transformation as it's essentially loss of information. This can lead to suboptimal training.
  
  Therefore your third approach is the best comparatively speaking.
  
  > **Comments:**
  > **username_0**: Hi, thanks for your response! I understand the third approach is the most suitable. However, I'm concerned that using tanh might limit exploration because the std could be constrained too much, leading to actions clustering near the boundaries of [-1, 1].  Do you think this could be a problem, and if so, do you have any suggestions to improve exploration, like adjusting the std range or using a different transformation?  
  > **username_1**: It's a valid concern, clip the learned std's for all states to a reasonable range to ensure learned Gaussian distributions not too wide since learning a wide std is useless anyway. Also you can add noise to the means to encourage exploration which is limited by the possible clustering near the boundaries of [-1,1]. And if your action constraints are not violated very often by untransformed raw Gaussian sampled actions from learned parameters for all states during PPO training, then you can also try 2nd method which is fine under such rare clip assumption. Try both and compare as this is an art  
  
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