Policy Learning Stability via Dynamic Weighting
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Solution Overview
Problem
Offline Reinforcement Learning (RL) faces challenges due to the mismatch between the dataset and the distribution of states and actions visited by the learned policy, leading to instability and divergence, particularly in low-data regions, where importance sampling methods suffer from high variance and unbounded errors.
Innovation Solution
A modified critic update rule that resamples TD updates with weights close to the replay-buffer distribution, satisfying the Non-Expansion Criterion for stable off-policy learning, and incorporates COL regularization to prevent over-optimism in low-support regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If importance sampling methods are used to address proportional mismatch, then the learned policy can diverge from the data policy, but variance grows exponentially in trajectory length causing instability
Solution Approach 1:
The patent introduces an intermediary weighting mechanism that mediates between the data distribution and policy distribution. Instead of directly applying importance sampling weights that cause exponential variance, the method uses a learned weighting function that interpolates between uniform weighting and importance sampling weights, thereby stabilizing learning while maintaining policy divergence capability
Solution Approach 2:
The patent changes the parameter of the weighting strategy from fixed importance sampling weights to dynamically learned weights. By parameterizing the weighting function and learning it alongside the policy, the method adapts the weighting to minimize variance while still addressing proportional mismatch, transforming a static high-variance approach into a dynamic low-variance approach
2Reliability
If the learned policy is constrained to be close to the data generating policy, then learning stability is improved, but performance of the learned policy deteriorates
Solution Approach 1:
The patent makes the policy constraint dynamic rather than static. Instead of enforcing a fixed KL-divergence constraint that limits performance, the method dynamically adjusts the effective constraint through learned weighting that allows the policy to explore further from the data distribution when beneficial, while maintaining stability through the learned weight adaptation mechanism
3Productivity
If TD updates are performed with standard replay buffer sampling, then data efficiency is maintained, but catastrophic divergence occurs when policy diverges from data policy
Solution Approach 1:
The patent introduces learned weighting as an intermediary between standard replay buffer sampling and TD updates. This weighting layer acts as a buffer that prevents catastrophic divergence by smoothly adjusting the influence of different samples, allowing the policy to diverge from the data policy without causing instability in the TD learning process
Data Source
AI summary
A computer-implemented method of learning a policy for an agent. The method includes: receiving an initialized first neural network, in particular a Q-functionor value-function, an initialized second neural network, auxiliary parameters, and the initialized policy; repeating the following steps until a termination condition is fulfilled: sampling a plurality of pairs of states, actions, rewards and new states from a storage. Sampling actions for the current states, and actions for the new sampled states; computing features from a penultimate layer of the first neural network based on the sampled states and actions and updating the second neural network and the auxiliary parameters as well as updating parameters the first neural network using a re-weighted loss.