Reinforcement Learning Policy Evaluation via Observation Compression
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Solution Overview
Problem
Reinforcement learning agents face challenges in efficiently and accurately estimating action values, limiting their performance due to reliance on parameterized models, which can be computationally intensive and less effective.
Innovation Solution
The method involves compressing training observations into partitions based on total rewards and actions, using probabilistic modeling to estimate action values, allowing for more powerful and efficient action selection policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameterized models are used to estimate action values, then the reinforcement learning agent can perform action selection, but the computational complexity increases and accuracy is limited
Solution Approach 1:
The patent creates compressed representations (copies) of training observations that capture essential information while reducing complexity. These compressed observations serve as simplified models that enable accurate action value estimation without requiring full parameterized models, thus resolving the contradiction between accuracy and computational complexity
Solution Approach 2:
The patent transforms the parameterization approach by changing from traditional parameterized value functions to a compression-based representation. By altering how observations are represented (through compression algorithms rather than parameterized models), the system achieves both accuracy and computational efficiency
2Productivity
If traditional action selection policies are used, then the reinforcement learning agent can operate with simple models, but performance is limited due to inaccurate action value estimation
Solution Approach 1:
By creating compressed copies of training observations, the system enables more accurate action value estimation that directly improves reinforcement learning performance. The compressed representations preserve critical information needed for accurate evaluation while maintaining computational tractability
Solution Approach 2:
The patent implements a feedback mechanism where compressed observations from training histories are used to evaluate action values, which then inform better action selections. This feedback loop continuously improves performance by leveraging accurate action value estimates derived from compressed representations
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating reinforcement learning policies. One of the methods includes receiving a plurality of training histories for a reinforcement learning agent; determining a total reward for each training observation in the training histories; partitioning the training observations into a plurality of partitions; determining, for each partition and from the partitioned training observations, a probability that the reinforcement learning agent will receive the total reward for the partition if the reinforcement learning agent performs the action for the partition in response to receiving the current observation; determining, from the probabilities and for each total reward, a respective estimated value of performing each action in response to receiving the current observation; and selecting an action from the pre-determined set of actions from the estimated values in accordance with an action selection policy.