Sample-efficient Reinforcement Learning in Robotic Table Tennis
Jonas Tebbe,Lukas Krauch,Yapeng Gao,Andreas Zell,Jonas Tebbe,Lukas Krauch,Yapeng Gao,Andreas Zell
Reinforcement learning (RL) has achieved some impressive recent successes in various computer games and simulations. Most of these successes are based on having large numbers of episodes from which the agent can learn. In typical robotic applications, however, the number of feasible attempts is very limited. In this paper we present a sample-efficient RL algorithm applied to the example of a table...
