Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

Abstract

Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based methods struggle to achieve the same asymptotic performance as model-free methods. In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a novel model-based approach---that can effectively improve the asymptotic performance using the uncertainty in Q-values. We derive an upper bound of the uncertainty, based on which we can approximate the uncertainty accurately and efficiently for model-based methods. We further propose an uncertainty-aware policy optimization algorithm that optimizes the policy conservatively to encourage performance improvement with high probability. This can significantly alleviate the overfitting of policy to inaccurate models. Experiments show POMBU can outperform existing state-of-the-art policy optimization algorithms in terms of sample efficiency and asymptotic performance. Moreover, the experiments demonstrate the excellent robustness of POMBU compared to previous model-based approaches.

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Continuous control with deep reinforcement learning
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Trust Region Policy Optimization
• Rainbow: Combining Improvements in Deep Reinforcement Learning
• Model-Ensemble Trust-Region Policy Optimization
• Model-Based Bayesian Exploration
• EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
• Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
• Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
• Generalization and Exploration via Randomized Value Functions
• The Uncertainty Bellman Equation and Exploration