RL Agent Training With Adversarial Sampling for Failure-Prone States
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Solution Overview
Problem
Reinforcement learning agents, such as those used in autonomous vehicles, face inefficiencies in training due to unbalanced data distribution, where 'harder' examples that lead to failures are rare and thus underrepresented, leading to unrobust policies and high costs of failure when deployed.
Innovation Solution
An adversarial sampling method is employed, where a failure predictor is trained to identify sequences of states with higher failure probabilities, allowing for stochastic sampling to focus training on these challenging scenarios, thereby improving the robustness and efficiency of the reinforcement learning agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uniform sampling of state space is used for training, then all possible states are covered, but challenging states with higher failure probability are underrepresented leading to unrobust policies
Solution Approach 1:
The patent changes the sampling parameter from uniform distribution to a non-uniform distribution weighted by failure probability. The training data sampling strategy is modified to prioritize states with higher failure probabilities, transforming the parameter distribution of training samples to better represent challenging scenarios.
Solution Approach 2:
Instead of uniformly sampling all states, the patent applies partial action by focusing training resources on a subset of challenging states identified by the failure predictor. This excessive focus on difficult examples (rather than treating all examples equally) improves robustness by deliberately over-sampling hard cases.
2Productivity
If training focuses on challenging states with higher failure probability, then sample efficiency improves, but requires additional complexity in determining and sampling these states
Solution Approach 1:
The patent introduces a failure predictor as an intermediary component that estimates failure probabilities of different states. This mediator enables the system to identify challenging states without requiring complex direct analysis, simplifying the overall approach while achieving targeted sampling of difficult examples.
Solution Approach 2:
The failure predictor is trained in advance on a dataset of states and their failure outcomes. This preliminary action creates a pre-computed model that can quickly estimate failure probabilities during training, avoiding the need for complex real-time analysis and enabling efficient sampling strategies.
3Reliability
If a failure predictor is trained to identify challenging states, then training can be focused on high-failure-probability sequences, but additional training steps and data collection are required
Solution Approach 1:
The failure predictor is trained using data generated from the RL agent's own interactions with the environment. The agent collects experience data during normal operation, which is then used to train the failure predictor, which in turn improves the agent's training. This self-service loop eliminates the need for external data collection or manual annotation processes.
Solution Approach 2:
The system implements a feedback loop where the failure predictor's predictions guide the sampling of training states, and the results of training on these states are used to update both the RL agent and the failure predictor. This continuous feedback mechanism improves prediction accuracy over time while integrating seamlessly into the training process.
Data Source
AI summary
Methods and systems of training RL agent for autonomous operation of a vehicle are described. The RL agent is trained using uniformly sampled training samples and learning a policy. After the RL agent has achieved a predetermined performance goal, data is collected including a sequence of sampled states, and for each sequence of sampled states, agent parameters, and an indication of failure of the RL agent for the sequence. A failure predictor is trained, using samples from the collected data, to predict a probability of failure of the RL agent for a given sequence of states. Sequences of states are collected by simulating interaction of the vehicle with the environment. Based on a probability of failure outputted by the failure predictor, a sequence of states is selected. The RL agent is further trained based on the selected sequence of states.