Bidirectional Model-based Policy Optimization
This addresses the challenge of inaccurate forward models in reinforcement learning, offering a novel approach to reduce reliance on model accuracy, though it appears incremental as it builds on existing model-based methods.
The paper tackles the problem of model error in model-based reinforcement learning by introducing a backward dynamics model alongside the forward model, resulting in improved sample efficiency and asymptotic performance compared to state-of-the-art methods.
Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate. Although there are several existing methods dedicated to combating the model error, the potential of the single forward model is still limited. In this paper, we propose to additionally construct a backward dynamics model to reduce the reliance on accuracy in forward model predictions. We develop a novel method, called Bidirectional Model-based Policy Optimization (BMPO) to utilize both the forward model and backward model to generate short branched rollouts for policy optimization. Furthermore, we theoretically derive a tighter bound of return discrepancy, which shows the superiority of BMPO against the one using merely the forward model. Extensive experiments demonstrate that BMPO outperforms state-of-the-art model-based methods in terms of sample efficiency and asymptotic performance.