Practical Probabilistic Model-based Deep Reinforcement Learning by
  Integrating Dropout Uncertainty and Trajectory Sampling
Wenjun Huang Yunduan Cui Huiyun Li Xinyu Wu
This paper addresses the prediction stability, prediction accuracy and
control capability of the current probabilistic model-based reinforcement
learning (MBRL) built on neural networks. A novel approach dropout-based
probabilistic ensembles with trajectory sampling (DPETS) is proposed where the
system uncertainty is stably predicted by combining the Monte-Carlo dropout and
trajectory sampling in one framework. Its loss function is designed to correct
the fitting error of neural networks for more accurate prediction of
probabilistic models. The state propagation in its policy is extended to filter
the aleatoric uncertainty for superior control capability. Evaluated by several
Mujoco benchmark control tasks under additional disturbances and one practical
robot arm manipulation task, DPETS outperforms related MBRL approaches in both
average return and convergence velocity while achieving superior performance
than well-known model-free baselines with significant sample efficiency. The
open source code of DPETS is available at https://github.com/mrjun123/DPETS.