Distributed Fusion-Based Policy Search for Fast Robot Locomotion Learning.

Deep reinforcement learning methods are developed to deal with challenging locomotion control problems in a robotics domain and can achieve significant performance improvement over conventional control methods. One of their appealing advantages is model-free. In other words, agents learn a control policy completely from scratches with raw high-dimensional sensory observations. However, they often ...