Robust Entropy-Regularized Reinforcement Learning Under Model Misspecification
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Solution Overview
Problem
Reinforcement learning agents trained in a single environment are sensitive to perturbations in the test environment, leading to sub-optimal performance when deployed in different real-world settings due to model misspecification, such as changes in lighting, sensor noise, and actuator noise.
Innovation Solution
A method of training a policy neural network using a robust entropy-regularized temporal difference error to account for potential perturbations, jointly with a Q-value neural network, to enhance the agent's robustness to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning agents are trained in a single environment, then training simplicity is maintained, but performance robustness deteriorates when deployed in different real-world settings
Solution Approach 1:
The patent applies preliminary action by incorporating perturbation handling into the training phase. Specifically, the training process artificially introduces environmental perturbations (changes in lighting, sensor noise, actuator noise) before deployment, allowing the agent to pre-adapt to various conditions. This resolves the contradiction by building robustness in advance during training rather than requiring complex adaptive mechanisms during deployment.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying environmental parameters during training. The training environment dynamically adjusts parameters such as lighting conditions, sensor noise levels, and actuator characteristics to create diverse training scenarios. This exposes the agent to a range of conditions, improving performance robustness without requiring multiple separate training systems.
2Measurement precision
If the training environment perfectly matches the test environment, then model accuracy is maximized, but adaptability to real-world variations deteriorates
Solution Approach 1:
The patent applies dynamics by transforming the static training environment into a dynamic one that continuously varies environmental conditions. The training system introduces time-varying perturbations including lighting changes, sensor noise variations, and actuator inconsistencies. This dynamic training process teaches the agent to handle environmental variations while maintaining accurate policy learning, thus improving generalization without sacrificing model accuracy.
Solution Approach 2:
The patent uses an intermediary approach by introducing a perturbation layer between the agent and the environment. This intermediary component artificially generates environmental variations during training, acting as a mediator that bridges the gap between controlled training conditions and unpredictable real-world settings. The agent learns to compensate for these intermediate perturbations, improving its ability to generalize to unseen environments.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network having policy parameters. One of the methods includes sampling a mini-batch comprising one or more observation – action – reward tuples generated as a result of interactions of a first agent with a first environment; determining an update to current values of the Q network parameters by minimizing a robust entropy-regularized temporal difference (TD) error that accounts for possible perturbations of the states of the first environment represented by the observations in the observation – action – reward tuples; and determining, using the Q-value neural network, an update to the policy network parameters using the sampled mini-batch of observation – action – reward tuples.