CS 188: Artificial Intelligence Spring 2007

1. CS 188: Artificial IntelligenceSpring 2007 Lecture 23: Reinforcement Learning: III 4/17/2007 Srini Narayanan – ICSI and UC Berkeley

2. Announcements • Othello tournament rules up. • On-line readings for this week.

3. Value Iteration • Idea: • Start with bad guesses at all utility values (e.g. V0(s) = 0) • Update all values simultaneously using the Bellman equation (called a value update or Bellman update): • Repeat until convergence • Theorem: will converge to unique optimal values • Basic idea: bad guesses get refined towards optimal values • Policy may converge long before values do

4. Example: Bellman Updates

5. Example: Value Iteration • Information propagates outward from terminal states and eventually all states have correct value estimates [DEMO]

6. Policy Iteration • Alternate approach: • Policy evaluation: calculate utilities for a fixed policy until convergence (remember last lecture) • Policy improvement: update policy based on resulting converged utilities • Repeat until policy converges • This is policy iteration • Can converge faster under some conditions

7. Policy Iteration • If we have a fixed policy , use simplified Bellman equation to calculate utilities: • For fixed utilities, easy to find the best action according to one-step look-ahead

8. Comparison • In value iteration: • Every pass (or “backup”) updates both utilities (explicitly, based on current utilities) and policy (possibly implicitly, based on current policy) • In policy iteration: • Several passes to update utilities with frozen policy • Occasional passes to update policies • Hybrid approaches (asynchronous policy iteration): • Any sequences of partial updates to either policy entries or utilities will converge if every state is visited infinitely often

9. Reinforcement Learning • Reinforcement learning: • Still have an MDP: • A set of states s  S • A set of actions (per state) A • A model T(s,a,s’) • A reward function R(s,a,s’) • Still looking for a policy (s) • New twist: don’t know T or R • I.e. don’t know which states are good or what the actions do • Must actually try actions and states out to learn [DEMO]

10. Example: Animal Learning • RL studied experimentally for more than 60 years in psychology • Rewards: food, pain, hunger, drugs, etc. • Mechanisms and sophistication debated • Example: foraging • Bees learn near-optimal foraging plan in field of artificial flowers with controlled nectar supplies • Bees have a direct neural connection from nectar intake measurement to motor planning area

11. Passive Learning • Simplified task • You don’t know the transitions T(s,a,s’) • You don’t know the rewards R(s,a,s’) • You are given a policy (s) • Goal: learn the state values (and maybe the model) • In this case: • No choice about what actions to take • Just execute the policy and learn from experience • We’ll get to the general case soon

12. Example: Direct EstimationSimple Monte Carlo y • Episodes: +100 (1,1) up -1 (1,2) up -1 (1,2) up -1 (1,3) right -1 (2,3) right -1 (3,3) right -1 (3,2) up -1 (3,3) right -1 (4,3) exit +100 (done) (1,1) up -1 (1,2) up -1 (1,3) right -1 (2,3) right -1 (3,3) right -1 (3,2) up -1 (4,2) exit -100 (done) -100 x  = 1, R = -1 U(1,1) ~ (92 + -106) / 2 = -7 U(3,3) ~ (99 + 97 + -102) / 3 = 31.3

13. Model-Based Learning • Idea: • Learn the model empirically (rather than values) • Solve the MDP as if the learned model were correct • Empirical model learning • Simplest case: • Count outcomes for each s,a • Normalize to give estimate of T(s,a,s’) • Discover R(s,a,s’) the first time we experience (s,a,s’) • More complex learners are possible (e.g. if we know that all squares have related action outcomes, e.g. “stationary noise”)

14. Example: Model-Based Learning y • Episodes: +100 (1,1) up -1 (1,2) up -1 (1,2) up -1 (1,3) right -1 (2,3) right -1 (3,3) right -1 (3,2) up -1 (3,3) right -1 (4,3) exit +100 (done) (1,1) up -1 (1,2) up -1 (1,3) right -1 (2,3) right -1 (3,3) right -1 (3,2) up -1 (4,2) exit -100 (done) -100 x  = 1 T(<3,3>, right, <4,3>) = 1 / 3 T(<2,3>, right, <3,3>) = 2 / 2

15. Model-Based Learning • In general, want to learn the optimal policy, not evaluate a fixed policy • Idea: adaptive dynamic programming • Learn an initial model of the environment: • Solve for the optimal policy for this model (value or policy iteration) • Refine model through experience and repeat • Crucial: we have to make sure we actually learn about all of the model

16. Example: Greedy ADP • Imagine we find the lower path to the good exit first • Some states will never be visited following this policy from (1,1) • We’ll keep re-using this policy because following it never collects the regions of the model we need to learn the optimal policy ? ?

17. What Went Wrong? • Problem with following optimal policy for current model: • Never learn about better regions of the space if current policy neglects them • Fundamental tradeoff: exploration vs. exploitation • Exploration: must take actions with suboptimal estimates to discover new rewards and increase eventual utility • Exploitation: once the true optimal policy is learned, exploration reduces utility • Systems must explore in the beginning and exploit in the limit ? ?

18. T T T T T T T T T T Dynamic Programming T T T

19. T T T T T T T T T T T T T T T T T T T T Simple Monte Carlo

20. T T T T T T T T T T T T T T T T T T T T Simplest TD Method

21. s a s, a s,a,s’ s’ Model-Free Learning • Big idea: why bother learning T? • Update each time we experience a transition • Frequent outcomes will contribute more updates (over time) • Temporal difference learning (TD) • Policy still fixed! • Move values toward value of whatever successor occurs

22. TD Learning features • On-line • Incremental • Bootstrapping (like DP unlike MC) • Model free • Does not require special treatment of exploration actions (unlike DP) • Converges for any policy to the correct value of a state for that policy. • As long as • Alpha is high in the beginning and low at the end (say 1/k) • Gamma less than one

23. s a s, a s,a,s’ s’ Problems with TD Value Learning • TD value learning is model-free for policy evaluation • However, if we want to turn our value estimates into a policy, we’re sunk: • Idea: Learn state-action pairings (Q-values) directly • Makes action selection model-free too!

24. Q-Functions • To simplify things, introduce a q-value, for a state and action under a policy • Utility of taking starting in state s, taking action a, then following  thereafter

25. The Bellman Equations • Definition of utility leads to a simple relationship amongst optimal utility values: Optimal rewards = maximize over first action and then follow optimal policy • Formally:

26. Q-Learning • Learn Q*(s,a) values • Receive a sample (s,a,s’,r) • Consider your old estimate: • Consider your new sample estimate: • Nudge the old estimate towards the new sample:

27. Q-Learning

28. Exploration / Exploitation • Several schemes for forcing exploration • Simplest: random actions (-greedy) • Every time step, flip a coin • With probability , act randomly • With probability 1-, act according to current policy • Problems with random actions? • You do explore the space, but keep thrashing around once learning is done • One solution: lower  over time • Another solution: exploration functions

29. Demo of Q Learning • Demo Robot crawling • Parameters • a = 1/k (learning rate) • g = discounted reward (high for future rewards) • e = exploration(should decrease with time) • MDP • number of pixel moved to the right/ iteration number • Actions : Arm up and down (yellow line), hand up and down (red line)

30. Q-Learning Properties • Will converge to optimal policy • If you explore enough • If you make the learning rate small enough • Neat property: does not learn policies which are optimal in the presence of action selection noise