Model-Based Reinforcement Learning via Meta-Policy Optimization
This addresses the data efficiency problem in reinforcement learning for AI applications, offering a robust incremental improvement over prior model-based methods.
The paper tackles the challenge of model-based reinforcement learning achieving lower asymptotic performance than model-free methods due to inaccurate dynamics models, and proposes MB-MPO, which uses an ensemble of models to meta-learn a policy that adapts quickly, matching model-free performance with significantly less data.
Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve the same asymptotic performance as model-free methods. We propose Model-Based Meta-Policy-Optimization (MB-MPO), an approach that foregoes the strong reliance on accurate learned dynamics models. Using an ensemble of learned dynamic models, MB-MPO meta-learns a policy that can quickly adapt to any model in the ensemble with one policy gradient step. This steers the meta-policy towards internalizing consistent dynamics predictions among the ensemble while shifting the burden of behaving optimally w.r.t. the model discrepancies towards the adaptation step. Our experiments show that MB-MPO is more robust to model imperfections than previous model-based approaches. Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.