Adaptive Return Computation for Reinforcement Learning Exploration
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Solution Overview
Problem
Existing machine learning models face challenges in efficiently training neural networks for task performance due to inadequate selection of return computation schemes, leading to suboptimal resource consumption and performance, particularly in environments requiring exploration and exploitation balance.
Innovation Solution
An adaptive mechanism is employed to dynamically select the most appropriate return computation scheme during training, using separate neural networks for extrinsic and intrinsic rewards, and adjusting policies to optimize exploration and exploitation, thereby reducing resource consumption and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed return computation scheme is used throughout training, then the training process is simple and stable, but the agent cannot adapt to different exploration needs at different training stages
Solution Approach 1:
The patent implements dynamic adaptation of return computation schemes by maintaining a policy that selects different schemes at different training stages. The system transitions from fixed to dynamic scheme selection, allowing the agent to adapt exploration behavior to current training needs without requiring manual intervention.
Solution Approach 2:
The system uses feedback from training progress to automatically adjust the return computation scheme selection. By monitoring training outcomes and agent performance, the policy learns which schemes are most effective at different stages and adjusts accordingly, creating a self-regulating training process.
2Productivity
If multiple return computation schemes are manually selected and switched, then exploration and exploitation can be optimized at different stages, but the system complexity and manual intervention requirements increase
Solution Approach 1:
The system performs self-service by automatically selecting and switching between return computation schemes based on training progress. The maintained policy evaluates training outcomes and autonomously determines when to transition between schemes, eliminating the need for manual intervention while optimizing training efficiency.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor training progress and agent performance, using this information to automatically adjust scheme selection. This closed-loop approach ensures the system adapts to changing training needs without external intervention.
3Reliability
If a single neural network handles both extrinsic and intrinsic rewards, then the architecture is simpler, but training stability and convergence are reduced
Solution Approach 1:
The patent applies segmentation by dividing the neural network into separate components: one for processing extrinsic rewards and another for intrinsic rewards. This modular architecture allows each network to specialize in its specific reward type, improving training stability and convergence while maintaining manageable complexity through structured organization.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning with adaptive return computation schemes. In one aspect, a method includes: maintaining data specifying a policy for selecting between multiple different return computation schemes, each return computation scheme assigning a different importance to exploring the environment while performing an episode of a task; selecting, using the policy, a return computation scheme from the multiple different return computation schemes; controlling an agent to perform the episode of the task to maximize a return computed according to the selected return computation scheme; identifying rewards that were generated as a result of the agent performing the episode of the task; and updating, using the identified rewards, the policy for selecting between multiple different return computation schemes.