Dynamic Locomotion Skills for Obstacle Sequences Using Reinforcement Learning
نویسندگان
چکیده
Most locomotion control strategies are developed for flat terrain. We explore the use of reinforcement learning to develop motor skills for the highly dynamic traversal of terrains having sequences of gaps, walls, and steps. Results are demonstrated using simulations of a 21-link planar dog and a 7-link planar biped. Our approach is characterized by: non-parametric representation of the value function and the control policy; value iteration using batched positive-TD updates; localized epsilon-greedy exploration; and an optimized state distance metric. The policies are progressively improved using repeated iterations of epsilon-greedy exploration and value iteration.
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