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Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning by Wang, Pengqin; Zhu, Meixin; Shen, Shaojie is a scholarly article available to read on EtoBox.

What is Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning about?

Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible solution. On the one hand, it eliminates the requirements of interaction with the actual environment. On the other hand, it learns the transition dynamics and reward function from the offline datasets and generates simulated rollouts to accelerate training. Previous model-based offline RL methods adopt probabilistic ensemble neural networks (NN) to model aleatoric uncertainty and epistemic uncertainty. However, this results in an exponential increase in training time and computing resource requirements. Furthermore, these methods are easily disturbed by the accumulative errors of the environment dynamics models when simulating long-term rollouts. To solve the above problems, we propose an uncertainty-aware sequence modeling architecture called Environment Transformer. It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter. Benefiting from the accurate modeling of the transition dyn

Author

Wang, Pengqin; Zhu, Meixin; Shen, Shaojie

Published

2023

Language

EN