Learning to Walk via Deep Reinforcement Learning.

Deep reinforcement learning offers the promise of automatic acquisition of robotic control policies that directly map sensory inputs to low-level actions. In the domain of robotic locomotion, it could make it possible for locomotion skills to be learned with minimal engineering and without even needing to construct a model of the robot. However, applying deep reinforcement learning methods on real-world robots is exceptionally difficult, due both to the sample complexity and, just as importantly, the sensitivity of such methods to hyperparameters. While hyperparameter tuning can be performed in parallel in simulated domains, it is usually impractical to tune hyperparameters directly on real-world robotic platforms, especially legged platforms like quadrupedal robots that can be damaged through extensive trial-and-error learning. We develop a stable deep RL algorithm that extends soft actor-critic, requires minimal hyperparameter tuning, and requires only a modest number of trials to learn multilayer neural network policies. We then apply this method to learn walking gaits on a real-world Minitaur robot. Our method can learn to walk from scratch directly in the real world in two hours of training, without any model or simulation, and the resulting policy is robust to moderate variations in the environment. We further show that our algorithm achieves state-of-the-art performance on four standard simulated benchmarks.