7.1.Reinforcement Learning

# 7.1.Reinforcement Learning - RL Introductory References...

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1 Machine Learning and Data Mining COMP9417 Reinforcement Learning (RL) Session I 2011 LIC: Mike Bain Guest Lecturer: Bernhard Hengst email: RL Introductory References References: Artificial Intelligence: A Modern Approach (Second Edition) Stuart Russell and Peter Norvig (Chapter 21) Machine Learning T. Michell, 1997 (Chapters 1 and 13) Reinforcement Learning: An Introduction R Sutton and A G. Barto 1998 (html version – link from Sutton’s home page) Part 1 Introduction to Reinforcement Learning (RL) Intuition Agent view of RL Simple example introducing concepts Markov Decision Problems (MDP) Why Markov? Stochastic problems Infinite horizon problems Exploration vs Exploitation Function approximation Unknown model and Q-Learning Pole Balancing Example Reinforcement learning is about: - how a machine can learn - the best way to act - given future rewards. Chapter 1 Machine Learning T. Michell, 1997 Task T = checkers Performance P = wins Representation of board state x = (x 1 , x 2 , …x 6 ) Target value function V(board ) = ! i w i x i Update rule V(board) = V(successor(board)) In any board state, play best V(successor(board)) The Agent View of RL agent Environment Sensors effectors One Room Problem Room States 0 1 2 3 4 5 6 7 8 Exit Reward \$100 cost \$1 per time-step Actions {N,S,E,W} Objective or goal: find a set of actions to maximise reward over time

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2 Sense – Act Cycle 0 1 2 3 4 5 6 7 8 agent Environment Reward, State action Policy " : S ! A e.g. " (1)=E, " (2)=S, " (5)=S, ... \$93 \$98 \$99 \$94 \$97 \$100 \$95 \$96 \$95 Value Function is the utility of the current state in terms of future rewards given a policy. 0 1 2 3 4 5 6 7 8 \$100 -\$1 policy " = Sum of rewards to termination (Stochastic Shortest Path) Sum of rewards for next N time steps Discounted sum of rewards Average reward per step Optimality Criteria Optimal Value Function \$97 \$98 \$99 \$98 \$99 \$100 \$97 \$98 \$99 0 1 2 3 4 5 6 7 8 \$100 -\$1 (*=Optimal) An Optimal Policy \$97 \$98 \$99 \$98 \$99 \$100 \$97 \$98 \$99 0 1 2 3 4 5 6 7 8 Solution - Value Iteration
3 \$100 the only reward 0 1 2 3 4 5 6 7 8 Be Careful Defining the Problem! * \$100 * \$100 * \$100 * \$100 * \$100 * \$100 * \$100 * \$100 * \$100 \$0 Discounted Value Function * \$72.9 * \$81 * \$90 * \$81 * \$90 * \$100 * \$72.9 * \$81 * \$90 0 1 2 3 4 5 6 7 8 \$100 \$0 Example History of Reinforcement Learning • Psychology/biology (eg B F Skinner - animal training) Operations Research Dynamic Programming (planning over time) MENACE (Machine Educable Noughts and Crosses Engine – D.Michie, 1961)

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4 Other RL Examples Checker Player [Arthur Samuel, 1959,1967] Trial and Error [Michie, 1961] TD-Gammon [Tesauro, 1995]
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