Corrected Tree Search Policy for Distribution Shift in Reinforcement Learning
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Solution Overview
Problem
Current tree search techniques in reinforcement learning are limited by distribution shift and scalability issues, particularly in continuous state-spaces and image-based domains, leading to sub-optimal action selection and computational inefficiencies.
Innovation Solution
Implementing a corrected Tree-Search (TS) policy using a correction term based on Bellman error and parallel processing units (PPUs) to improve tree expansion efficiency and accuracy, reducing the impact of distribution shift and enabling deeper tree searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard MCTS is applied to continuous state-spaces or image-based domains, then the method cannot be applied to improve pre-trained agents, but the patent enables application to these domains through value function correction
Solution Approach 1:
The patent introduces a value function as an intermediary component that bridges MCTS and continuous state-spaces. The value function estimates state values and corrects distribution shift, enabling MCTS to operate reliably in domains where it previously failed. This intermediary allows the system to handle pre-trained agents without requiring visitation statistics collection.
2Measurement precision
If tree depth is increased to improve search accuracy, then better actions can be selected, but computational complexity increases exponentially
Solution Approach 1:
The patent changes the parameter of tree depth from a fixed large value to a variable that can be dynamically adjusted. By using moderate tree depths combined with value function correction, the system achieves high accuracy without exponential computational complexity. The value function compensates for limited search depth, allowing accurate action selection with computationally feasible tree expansions.
3Reliability
If tree depth is increased to reduce distribution shift, then better policy correction is achieved, but the TS process becomes computationally intensive
Solution Approach 1:
The value function serves as a mediator that reduces distribution shift without requiring deep tree searches. It corrects the policy by estimating state values and adjusting for distribution differences between training and test environments, achieving reliable action selection with computationally efficient moderate-depth trees.
4Productivity
If moderate tree depths are used to improve computational efficiency, then faster processing is achieved, but distribution shift occurs leading to sub-optimal actions
Solution Approach 1:
The value function provides feedback that corrects for distribution shift caused by moderate tree depths. It estimates the difference between the pre-trained policy and the tree-based policy, using this feedback to adjust action selection and compensate for the limitations of shallow search, thereby maintaining high accuracy with computational efficiency.
Data Source
AI summary
A method for performing a Tree-Search (TS) on an environment is provided. The method comprises generating a tree for a current state of the environment based on a TS policy, determining a corrected TS policy, and determining an action to apply to the environment based on the corrected TS policy. The tree comprises a plurality of nodes including a root node among the plurality of nodes corresponding to the current state of the environment. Each node other than the root node among the plurality of nodes corresponding to an estimated future state of the environment. The plurality of nodes in the tree are connected by a plurality of edges. Each edge among the plurality of edges is associated with an action causing a transition from a first state to a different sate of the environment.