Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling
This work addresses stability and efficiency issues in model-based reinforcement learning for robotics and control tasks, representing an incremental improvement over existing methods.
The paper tackles prediction stability, accuracy, and control capability in probabilistic model-based reinforcement learning by proposing DPETS, which integrates dropout uncertainty and trajectory sampling. It outperforms related MBRL approaches in average return and convergence velocity on Mujoco benchmarks and a robot arm task, achieving superior sample efficiency compared to model-free baselines.
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.