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

• URL: http://arxiv.org/abs/2102.06961v1
• Date: Sat, 13 Feb 2021 17:16:41 GMT
• Title: PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
• Authors: Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang
• Abstract summary: We propose a model-based offline reinforcement learning (RL) approach called PerSim. We first learn a personalized simulator for each agent by collectively using the historical trajectories across all agents prior to learning a policy. This representation suggests a simple, regularized neural network architecture to effectively learn the transition dynamics per agent, even with scarce, offline data.
• Score: 19.026312915461553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 a model-based offline RL approach, called PerSim, where we first learn 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 state dynamics prediction and eventual reward, confirms the efficacy of our framework in leveraging limited historical data to simultaneously learn personalized policies across agents.

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