CS6700 : Reinforcement Learning copy 2

The Reinforcement Learning problem : evaluative feedback, non-associative learning, Rewards and returns, Markov Decision Processes, Value functions, optimality and approximation. Dynamic programming : value iteration, policy iteration, asynchronous DP, generalized policy iteration. Monta-Carlo methods : policy evaluation, roll outs, on policy and off policy learning, importance sampling. Temporal Difference learning : TD prediction, Optimality of TD(0), SARSA, Q-learning, R-learning, Games and after states. Eligibility traces : n-step TD prediction, TD (lambda), forward and backward views, Q (lambda), SARSA (lambda), replacing traces and accumulating traces. Function Approximation : Value prediction, gradient descent methods, linear function approximation, ANN based function approximation, lazy learning, instability issues Policy Gradient methods : non-associative learning – REINFORCE algorithm, exact gradient methods, estimating gradients, approximate policy gradient algorithms, actor-critic methods. Planning and Learning : Model based learning and planning, prioritized sweeping, Dyna, heuristic search, trajectory sampling, E 3 algorithm Hierarchical RL : MAXQ framework, Options framework, HAM framework, airport algorithm, hierarchical policy gradient Case studies : Elevator dispatching, Samuel’s checker player, TDgammon, Acrobot, Helicopter piloting