Bayes-Adaptive Deep Model-Based Policy Optimisation

Abstract

We introduce a Bayesian (deep) model-based reinforcement learning method (RoMBRL) that can capture model uncertainty to achieve sample-efficient policy optimisation. We propose to formulate the model-based policy optimisation problem as a Bayes-adaptive Markov decision process (BAMDP). RoMBRL maintains model uncertainty via belief distributions through a deep Bayesian neural network whose samples are generated via stochastic gradient Hamiltonian Monte Carlo. Uncertainty is propagated through simulations controlled by sampled models and history-based policies. As beliefs are encoded in visited histories, we propose a history-based policy network that can be end-to-end trained to generalise across history space and will be trained using recurrent Trust-Region Policy Optimisation. We show that RoMBRL outperforms existing approaches on many challenging control benchmark tasks in terms of sample complexity and task performance. The source code of this paper is also publicly available on https://github.com/thobotics/RoMBRL.

arXiv commentSource code https://github.com/thobotics/RoMBRL

References in corpus (10)

• Trust Region Policy Optimization
• Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
• Stochastic Gradient Hamiltonian Monte Carlo
• Learning Continuous Control Policies by Stochastic Value Gradients
• Model-Ensemble Trust-Region Policy Optimization
• Bayesian Dark Knowledge
• Model-Based Active Exploration
• Relative Entropy Regularized Policy Iteration
• Variational Inference MPC for Bayesian Model-based Reinforcement Learning
• Monte Carlo Bayesian Reinforcement Learning