Tutorial: Reinforcement Learning for Robotics and Control
TechTalks from event: Tutorial: Reinforcement Learning for Robotics and Control
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Tutorial: Reinforcement Learning for Robitcs and Control - Morning SessionThis all-day tutorial introduces the audience to reinforcement learning. Prior experience in this area is not assumed. In the first half of this tutorial we will cover the foundations of reinforcement learning: Markov decision processes, value iteration, policy iteration, linear programming for solving an MDP, function approximation, model-free versus model-based learning, Q-learning, TD-learning, policy search, the likelihood ratio policy gradient, the policy gradient theorem, actor-critic, natural gradient and importance sampling. In the second half of this tutorial we will discuss example success stories and open problems.
Tutorial: Reinforcement Learning for Robitcs and Control - Afternoon SessionPart-2 of the tutorial on Reinforcement Learning for Robotics and Control - presented at ICRA 2012.