1. Reinforcement Learning Dynamic Programming I Subramanian Ramamoorthy School of Informatics 31 January, 2012

2. Continuing from last time… MDPs and Formulation of RL Problem 31/01/20122

3. Returns Episodic tasks: interaction breaks naturally into episodes, e.g., plays of a game, trips through a maze. where T is a final time step at which a terminal state is reached, ending an episode. 31/01/20123

4. Returns for Continuing Tasks Continuing tasks: interaction does not have natural episodes. Discounted return: 31/01/20124

5. Example – Setting Up Rewards Avoid failure: the pole falling beyond a critical angle or the cart hitting end of track. As an episodic task where episode ends upon failure: As a continuing task with discounted return: In either case, return is maximized by avoiding failure for as long as possible. 31/01/20125

6. Another Example Get to the top of the hill as quickly as possible. Return is maximized by minimizing number of steps to reach the top of the hill. 31/01/20126

7. A Unified Notation In episodic tasks, we number the time steps of each episode starting from zero. We usually do not have to distinguish between episodes, so we write instead of for the state at step t of episode j. Think of each episode as ending in an absorbing state that always produces reward of zero: We can cover all cases by writing 31/01/20127

8. Markov Decision Processes If a reinforcement learning task has the Markov Property, it is basically a Markov Decision Process (MDP). If state and action sets are finite, it is a finite MDP. To define a finite MDP, you need to give: – state and action sets – one-step “dynamics” defined by transition probabilities: – reward probabilities: 31/01/20128

9. The RL Problem Main Elements: States, s Actions, a State transition dynamics - often, stochastic & unknown Reward (r) process - possibly stochastic Objective: Policy  t (s,a) – probability distribution over actions given current state Assumption: Environment defines a finite-state MDP 31/01/20129

10. Recycling Robot An Example Finite MDP At each step, robot has to decide whether it should (1) actively search for a can, (2) wait for someone to bring it a can, or (3) go to home base and recharge. Searching is better but runs down the battery; if it runs out of power while searching, it has to be rescued (which is bad). Decisions made on basis of current energy level: high, low. Reward = number of cans collected 31/01/201210

11. Recycling Robot MDP 31/01/201211

12. Enumerated in Tabular Form 31/01/201212

13. Given such an enumeration of transitions and corresponding costs/rewards, what is the best sequence of actions? We know we want to maximize: So, what must one do? 31/01/201213

14. The Shortest Path Problem 31/01/201214

15. Finite-State Systems and Shortest Paths – state space s k is a finite set for each k – a k can get you from s k to f k (s k, a k ) at a cost g k (x k, u k ) 31/01/201215 Length ≈ Cost ≈ Sum of length of arcs Solve this first J k (i) = min j [ a k ij + J k+1 (j)]

16. Value Functions The value of a state is the expected return starting from that state; depends on the agent’s policy: The value of taking an action in a state under policy  is the expected return starting from that state, taking that action, and thereafter following  : 31/01/201216

17. Recursive Equation for Value The basic idea: So: 31/01/201217

18. Optimality in MDPs – Bellman Equation 31/01/201218

19. More on the Bellman Equation This is a set of equations (in fact, linear), one for each state. The value function for  is its unique solution. Backup diagrams: 31/01/201219

20. Bellman Equation for Q 31/01/201220

21. Gridworld Actions: north, south, east, west ; deterministic. If it would take agent off the grid: no move but reward = –1 Other actions produce reward = 0, except actions that move agent out of special states A and B as shown. State-value function for equiprobable random policy;  = 0.9 31/01/201221

22. Golf State is ball location Reward of –1 for each stroke until the ball is in the hole Value of a state? Actions: –putt (use putter) –driver (use driver) putt succeeds anywhere on the green 22

23. Optimal Value Functions For finite MDPs, policies can be partially ordered: There are always one or more policies that are better than or equal to all the others. These are the optimal policies. We denote them all  *. Optimal policies share the same optimal state-value function: Optimal policies also share the same optimal action-value function: This is the expected return for taking action a in state s and thereafter following an optimal policy. 31/01/201223

24. Optimal Value Function for Golf We can hit the ball farther with driver than with putter, but with less accuracy Q*(s, driver ) gives the value or using driver first, then using whichever actions are best 31/01/201224

25. Bellman Optimality Equation for V* The value of a state under an optimal policy must equal the expected return for the best action from that state: The relevant backup diagram: V* is the unique solution of this system of nonlinear equations. 31/01/201225

26. Recursive Form of V* 31/01/201226

27. Bellman Optimality Equation for Q* The relevant backup diagram: Q* is the unique solution of this system of nonlinear equations. 31/01/201227

28. Why Optimal State-Value Functions are Useful Any policy that is greedy with respect to V* is an optimal policy. Therefore, given V*, one-step-ahead search produces the long-term optimal actions. E.g., back to the gridworld (from your S+B book): ** 31/01/201228

29. What About Optimal Action-Value Functions? Given Q*, the agent does not even have to do a one-step-ahead search: 31/01/201229

30. Solving the Bellman Optimality Equation Finding an optimal policy by solving the Bellman Optimality Equation requires the following: – accurate knowledge of environment dynamics; – we have enough space and time to do the computation; – the Markov Property. How much space and time do we need? – polynomial in number of states (via dynamic programming), – BUT, number of states is often huge (e.g., backgammon has about 10 20 states) We usually have to settle for approximations. Many RL methods can be understood as approximately solving the Bellman Optimality Equation. 31/01/201230