Deep RL Training With Offline Data and Simulator Reward Shaping
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Solution Overview
Problem
Deep reinforcement learning (RL) algorithms face challenges in real-world applications due to high sample complexity, safety concerns during training, and the impracticality of using off-line data to learn optimal policies, while simulators are often inaccurate and costly to maintain, leading to conservative and inefficient policy learning.
Innovation Solution
A method that combines off-line data with simulators using a reward modification approach, where an ensemble of models is learned from off-line data to quantify uncertainty, and the reward function is adjusted based on the agreement or disagreement between the ensemble and simulator predictions to encourage exploration in accurate areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If deep RL algorithms are trained using real-world data collection, then sample efficiency is improved, but training time and operational costs increase significantly
Solution Approach 1:
The patent pre-processes and stores transition data from real-world operations in an offline dataset before training begins. This preliminary data collection allows the algorithm to learn from historical operations without requiring the system to be taken offline during training, thus maintaining operational capacity while enabling efficient training.
Solution Approach 2:
The patent creates a replay buffer that stores copies of past transitions (state, action, reward, next state) from real-world operations. During training, the algorithm samples from these copied transitions rather than collecting new data in real-time, dramatically reducing training time and operational disruption while maintaining learning quality.
2Productivity
If deep RL algorithms are trained using off-line data only, then training cost is reduced, but the ability to learn optimal policies deteriorates due to distributional shift
Solution Approach 1:
The patent implements a feedback mechanism where the replay buffer continuously incorporates new transitions from real-world operations. This ongoing feedback loop allows the algorithm to adapt to distributional shifts and learn updated optimal policies while still primarily training on offline data, maintaining both efficiency and policy optimality.
Solution Approach 2:
The patent makes the training dataset dynamic by combining static offline data with continuously updated online transitions in the replay buffer. This dynamic composition allows the training distribution to adapt over time, preventing distributional shift and ensuring the learned policies remain optimal as operational conditions change.
3Object-affected harmful factors
If simulators are used for training, then safety risks are reduced, but simulation accuracy and reliability deteriorate
Solution Approach 1:
The patent merges data from multiple sources including offline real-world data, online real-world transitions, and simulator-generated transitions into a unified replay buffer. This combination leverages the safety benefits of simulator training while compensating for simulator inaccuracies using authentic real-world data, thereby maintaining both safety and reliability.
Solution Approach 2:
The patent creates a composite training dataset that combines transitions from different sources with different characteristics. Just as composite materials combine properties of constituent materials, this composite dataset combines the safety of simulator data with the accuracy of real-world data, achieving both protection from harm and maintenance of reliability.
4Measurement precision
If more samples are collected during training, then learning accuracy is improved, but computational resources and training time increase
Solution Approach 1:
The patent implements continuous learning by maintaining a replay buffer that is continuously populated with new transitions during operations. This allows the algorithm to learn continuously from incoming data without requiring batch retraining, improving learning accuracy over time while distributing computational load efficiently and avoiding resource-intensive periodic retraining.
Data Source
AI summary
Example implementations described herein involve an approach to address an imperfect simulator challenge using off-line data plus reward modification. The proposed solution is robust to simulator error, and therefore, it requires less maintenance in keeping the simulators updated. Even when the simulators are accurate, it is costly to keep them accurate over time. Moreover, compared to other robust reinforcement learning algorithms, the proposed approach does not assume the distribution of uncertainties in the simulator are known. Less complexity leads to fewer potential errors as well as lower computational cost during the training. Finally, the proposed approach has better performance compared to the state-of-the-art methods (higher overall cumulative rewards).