Policy Neural Network Training for Robust Continuous Control
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Solution Overview
Problem
Reinforcement learning agents are sensitive to environment perturbations, leading to sub-optimal performance when deployed in real-world settings different from their training environments, due to model misspecification.
Innovation Solution
Training a policy neural network to account for perturbations by jointly optimizing it with a Q-value neural network using a robust entropy-regularized temporal difference error, which measures errors based on perturbed states to enhance robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning agents are trained in a single environment, then training simplicity is maintained, but the agents become sensitive to environment perturbations and perform sub-optimally when deployed in different environments
Solution Approach 1:
The patent applies preliminary action by incorporating perturbation handling into the training phase. Specifically, the system generates perturbed versions of training environments and trains the agent to handle these perturbations before actual deployment. This preliminary exposure to perturbations during training ensures the agent develops robustness against environmental changes when deployed in real-world settings different from the training environment.
2Productivity
If the agent is trained in simulation, then training efficiency is improved, but the policy becomes sensitive to real-world environment perturbations
Solution Approach 1:
The patent applies preliminary action by incorporating perturbation handling into the training phase. Specifically, the system generates perturbed versions of training environments and trains the agent to handle these perturbations before actual deployment. This preliminary exposure to perturbations during training ensures the agent develops robustness against environmental changes when deployed in real-world settings different from the training environment.
Solution Approach 2:
The patent converts the harmful effect of simulation-to-reality gap into a benefit by deliberately introducing perturbations during simulation training. The system uses perturbed training environments to teach the agent to handle real-world variations, turning the previously harmful sensitivity to perturbations into a beneficial robustness feature that improves deployment performance.
3Productivity
If the policy is optimized for the training environment, then training performance is maximized, but the policy fails to generalize to perturbed environments
Solution Approach 1:
The patent applies preliminary action by incorporating perturbation handling into the training phase. Specifically, the system generates perturbed versions of training environments and trains the agent to handle these perturbations before actual deployment. This preliminary exposure to perturbations during training ensures the agent develops robustness against environmental changes when deployed in real-world settings different from the training environment.
Solution Approach 2:
The patent applies universality by training the agent to perform the same task across multiple perturbed environment variations. Instead of optimizing for a single training environment, the system trains the policy to handle various perturbations (lighting changes, sensor noise, actuator noise), making the policy universally applicable across different environmental conditions while maintaining consistent task performance.
Data Source
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.