PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators

Abstract

We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance of state-of-the-art offline and model-based RL methods degrade significantly given such limited data availability, even for commonly perceived "solved" benchmark settings such as "MountainCar" and "CartPole". To address this challenge, we propose PerSim, a model-based offline RL approach which first learns a personalized simulator for each agent by collectively using the historical trajectories across all agents, prior to learning a policy. We do so by positing that the transition dynamics across agents can be represented as a latent function of latent factors associated with agents, states, and actions; subsequently, we theoretically establish that this function is well-approximated by a "low-rank" decomposition of separable agent, state, and action latent functions. This representation suggests a simple, regularized neural network architecture to effectively learn the transition dynamics per agent, even with scarce, offline data. We perform extensive experiments across several benchmark environments and RL methods. The consistent improvement of our approach, measured in terms of both state dynamics prediction and eventual reward, confirms the efficacy of our framework in leveraging limited historical data to simultaneously learn personalized policies across agents.

References in corpus (14)

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
• Conservative Q-Learning for Offline Reinforcement Learning
• Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
• Behavior Regularized Offline Reinforcement Learning
• MOPO: Model-based Offline Policy Optimization
• Benchmarking Model-Based Reinforcement Learning
• Provably Efficient Reinforcement Learning with Linear Function Approximation
• MOReL : Model-Based Offline Reinforcement Learning
• Exploring Model-based Planning with Policy Networks
• Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
• FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
• Provably Good Batch Reinforcement Learning Without Great Exploration
• Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation