Domain Randomization Distribution Learning for Reinforcement Learning
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Solution Overview
Problem
Current reinforcement learning (RL) systems face challenges in effectively training agents to handle diverse real-world situations due to the 'reality gap' between simulated and real-world environments, with domain randomization (DR) methods being sensitive to the selection of randomization distributions.
Innovation Solution
A method and system that simultaneously learn a domain randomization distribution and an agent policy using reinforcement learning, where the agent policy is trained over a range of simulated environmental parameters to optimize performance, allowing for better generalization to real-world scenarios with fewer resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If domain randomization is used to train RL agents in simulation, then the agents can be trained with fewer real-world data requirements, but the success is highly dependent on correct selection of randomization distribution
Solution Approach 1:
The patent automatically adjusts domain randomization parameters (distribution types, ranges, and combinations) during the training process based on agent performance feedback. This dynamic parameter adaptation eliminates the need for manual DR distribution selection while maintaining training effectiveness, directly resolving the contradiction between reduced real-world data needs and reliability concerns.
Solution Approach 2:
The system implements a feedback loop where agent performance in simulation is continuously monitored and used to refine the domain randomization distribution. This closed-loop approach ensures that the DR parameters are optimized based on actual training outcomes, making the process reliable without requiring expert manual configuration.
2Ease of operation
If traditional domain randomization with fixed distributions is used, then training can be simpler, but the agent may not generalize well to diverse real-world situations
Solution Approach 1:
The patent transforms the static, fixed domain randomization distribution into a dynamic system that adapts during training. The DR parameters evolve based on agent performance, allowing the training process to automatically explore diverse environmental conditions that improve generalization while maintaining operational simplicity through automation.
Solution Approach 2:
The system performs self-optimization of domain randomization parameters without external intervention. The training process automatically identifies which environmental variations are most beneficial for agent generalization and adjusts the DR distribution accordingly, eliminating the need for complex manual configuration while achieving superior adaptability.
3Reliability
If extensive real-world training data is collected, then agent performance can be improved, but the cost and time requirements increase significantly
Solution Approach 1:
The patent creates virtual copies of diverse real-world environments through domain randomization in simulation. By training agents on these synthesized virtual experiences with automatically optimized DR parameters, the system achieves real-world performance without the time and cost of collecting extensive actual real-world data.
Solution Approach 2:
The system performs preliminary training in simulation with automatically adapted domain randomization before any real-world deployment. This pre-training phase prepares the agent for real-world conditions without requiring extensive real-world data collection, significantly reducing overall training time and resources while maintaining high performance.
Data Source
AI summary
Method or system for reinforcement learning that simultaneously learns a DR distribution ϕ while optimizing an agent policy Π to maximize performance over the learned DR distribution; method or system for training a learning agent using data synthesized by a simulator based on both a performance of the learning agent and a range of parameters present in the synthesized data.