Currently, our MoveModule class uses a basic experience replay mechanism where transitions are sampled uniformly from the memory buffer. While this approach has been effective, it does not account for the varying significance of experiences, which may lead to less efficient learning.
Feature Request:
Implement Prioritized Experience Replay to optimize the sampling of experiences. By prioritizing transitions with higher temporal-difference (TD) errors, we can focus training on experiences that are more impactful, leading to faster and potentially more stable learning.
Proposed Solution:
TD Error Calculation: After each update, calculate the TD error for each transition in the buffer and store this error along with the transition.
Prioritized Sampling: Use the TD errors to assign a probability of selection to each experience, ensuring experiences with larger errors are more likely to be selected.
Proportional or Rank-Based Prioritization: Consider implementing either proportional prioritization (where experiences are weighted based on the magnitude of TD error) or rank-based prioritization (where experiences are ranked by error and sampled accordingly).
Adjustable Hyperparameters: Add parameters such as alpha (controls the degree of prioritization) and beta (controls the amount of importance-sampling correction) to fine-tune the sampling distribution.
Benefits:
Improved Sample Efficiency: The network can learn more effectively by focusing on more informative transitions.
Faster Convergence: By focusing on high-error samples, the model may converge faster and learn a more robust policy.
Enhanced Stability: Prioritized sampling can help in reducing variance in updates and lead to more stable training.
Potential Challenges:
Memory Management: The experience buffer now needs to maintain and update TD errors, potentially increasing memory requirements.
Computational Overhead: Calculating and updating priorities may add some computational complexity, especially for large buffers.
Additional Context:
Prioritized Experience Replay was introduced in the paper "Prioritized Experience Replay" by Schaul et al., which highlights its benefits for Deep Q-Learning.
Acceptance Criteria:
[ ] Implement TD error calculation and storage in the experience replay buffer.
[ ] Prioritize experience sampling based on TD errors.
[ ] Add adjustable hyperparameters alpha and beta for prioritization control.
[ ] Document the functionality and provide example usage in the code comments or README.
Currently, our
MoveModuleclass uses a basic experience replay mechanism where transitions are sampled uniformly from the memory buffer. While this approach has been effective, it does not account for the varying significance of experiences, which may lead to less efficient learning.Feature Request:
Implement Prioritized Experience Replay to optimize the sampling of experiences. By prioritizing transitions with higher temporal-difference (TD) errors, we can focus training on experiences that are more impactful, leading to faster and potentially more stable learning.
Proposed Solution:
alpha(controls the degree of prioritization) andbeta(controls the amount of importance-sampling correction) to fine-tune the sampling distribution.Benefits:
Potential Challenges:
Additional Context:
Prioritized Experience Replay was introduced in the paper "Prioritized Experience Replay" by Schaul et al., which highlights its benefits for Deep Q-Learning.
Acceptance Criteria:
alphaandbetafor prioritization control.