Meta-learned Intrinsic Reward System for RL Training Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional reinforcement learning systems rely solely on extrinsic rewards, which can lead to inefficient exploration and exploitation, particularly in non-stationary environments or when long-term rewards are sparse.
Innovation Solution
The implementation of a meta-learned intrinsic reward system that generates intrinsic rewards based on the agent's actions and environment state, encouraging both exploratory and exploitative behavior, and persisting across agent lifetimes to maximize long-term extrinsic rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reinforcement learning systems rely solely on extrinsic rewards, then the system structure remains simple, but exploration efficiency deteriorates particularly in non-stationary environments or when long-term rewards are sparse
Solution Approach 1:
The patent segments the reward system into two distinct components: extrinsic rewards from the environment and intrinsic rewards generated by an internal system. This segmentation allows the agent to receive both external feedback and internal motivation signals, improving exploration efficiency without completely redesigning the reinforcement learning framework.
Solution Approach 2:
The patent introduces an intrinsic reward system as an intermediary component that generates internal motivation signals based on the agent's actions and environment state. This intermediary bridges the gap between simple extrinsic reward structures and the need for efficient exploration, particularly in non-stationary environments where extrinsic rewards may be sparse or delayed.
2Productivity
If an intrinsic reward system is introduced to improve exploration, then exploration efficiency improves, but the system complexity increases
Solution Approach 1:
The intrinsic reward system is designed to be universal and persistent across multiple agent lifetimes and tasks. Rather than creating a separate system for each task, the same intrinsic reward mechanism serves multiple functions: encouraging exploration during training, maintaining performance in non-stationary environments, and generalizing across different tasks. This multi-functionality reduces the relative complexity burden.
Solution Approach 2:
The patent updates the intrinsic reward system parameters based on extrinsic reward values obtained from task performance. This parameter adaptation allows the system to dynamically adjust its internal motivation signals based on actual task outcomes, improving training speed while keeping the architectural complexity manageable through parameter-based control rather than structural complexity.
3Reliability
If the intrinsic reward system persists across agent lifetimes to maximize long-term rewards, then long-term performance improves, but the difficulty of training and managing the system increases
Solution Approach 1:
The intrinsic reward system maintains continuous operation across agent lifetimes, persisting beyond individual task completions. This continuity allows the system to accumulate knowledge and adjust parameters based on long-term patterns, improving reliability and long-term performance. The system continuously refines its intrinsic motivation signals based on accumulated experience rather than resetting with each agent lifetime.
Solution Approach 2:
The patent implements feedback mechanisms where the intrinsic reward system is updated based on extrinsic reward values obtained from task performance. This feedback loop allows the system to learn from long-term outcomes and adjust its internal motivation signals accordingly, improving long-term performance while managing training complexity through structured feedback rather than uncontrolled system growth.
Data Source
AI summary
There is described methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The reinforcement learning system comprises an agent configured to perform actions based upon a policy and an intrinsic reward system configured to generate intrinsic reward values for the agent based upon the actions taken by the agent. The method comprises training the reinforcement learning system based upon a plurality of tasks. The training comprises updating the agent's policy based upon the intrinsic reward values generated by the intrinsic reward system and updating the intrinsic reward system based upon an extrinsic reward value obtained based upon the task being performed by the agent. The training further comprises re-initializing the agent's policy when an expiration criterion associated with the agent is met.