1. September 15September 15 Multiagent learning using a variable learning rate Igor Kiselev, University of Waterloo M. Bowling and M. Veloso. Artificial Intelligence, Vol. 136, 2002, pp. 215 - 250.

2. University of Waterloo Page 2 Agenda Introduction Motivation to multi-agent learning MDP framework Stochastic game framework Reinforcement Learning: single-agent, multi-agent Related work: Multiagent learning with a variable learning rate Theoretical analysis of the replicator dynamics WoLF Incremental Gradient Ascent algorithm WoLF Policy Hill Climbing algorithm Results Concluding remarks

3. September 15September 15 Introduction Motivation to multi-agent learning

4. University of Waterloo Page 4 MAL is a Challenging and Interesting Task Research goal is to enable an agent effectively learn to act (cooperate, compete) in the presence of other learning agents in complex domains. Equipping MAS with learning capabilities permits the agent to deal with large, open, dynamic, and unpredictable environments Multi-agent learning (MAL) is a challenging problem for developing intelligent systems. Multiagent environments are non-stationary, violating the traditional assumption underlying single-agent learning

5. University of Waterloo Page 5 Reinforcement Learning Papers: Statistics Google Scholar

6. University of Waterloo Page 6 Various Approaches to Learning / Related Work Y. Shoham et al., 2003

7. September 15September 15 Preliminaries MDP and Stochastic Game Frameworks

8. University of Waterloo Page 8 Single-agent Reinforcement Learning Independent learners act ignoring the existence of others Stationary environment Learn policy that maximizes individual utility (“trial-error”) Perform their actions, obtain a reward and update their Q- values without regard to the actions performed by others Policy World, State Learning Algorithm Actions Observations, Sensations Rewards R. S. Sutton, 1997

9. University of Waterloo Page 9 Markov Decision Processes / MDP Framework T. M. Mitchell, 1997 Environment is a modeled as an MDP, defined by (S, A, R, T) S – finite set of states of the environment A(s) – set of actions possible in state s  S T: S×A → P – set transition function from state-action pairs to states R(s,s',a) – expected reward on transition ( s to s‘) P(s,s',a) – probability of transition from s to s'  – discount rate for delayed reward Each discrete time t = 0, 1, 2,... agent: observes state S t  S chooses action a t  A receives immediate reward r t, state changes to S t+1 r t +f = 0... s t t a r t +1 s a r t +2 s a r t +3 s... t +3 a t+f-1 a s t +f

10. University of Waterloo Page 10 Agent’s learning task – find optimal action selection policy Execute actions in environment, observe results, and learn to construct an optimal action selection policy that maximizes the agent's performance - the long-term Total Discounted Reward Find a policy   s   S  a  A(s) that maximizes the value (expected future reward) of each s : and each s,a pair: V (s) = E { r +  r +  r + s =s,  } rewards t +1 t +2 t +3 t... 2  Q (s,a) = E { r +  r +  r + s =s, a =a,  } t +1 t +2 t +3 t t... 2  T. M. Mitchell, 1997

11. University of Waterloo Page 11 Agent’s Learning Strategy – Q-Learning method Q-function - iterative approximation of Q values with learning rate β: 0≤ β<1 Q-Learning incremental process 1.Observe the current state s 2.Select an action with probability based on the employed selection policy 3.Observe the new state s′ 4.Receive a reward r from the environment 5.Update the corresponding Q-value for action a and state s 6.Terminate the current trial if the new state s′ satisfies a terminal condition; otherwise let s′→ s and go back to step 1

12. University of Waterloo Page 12 Multi-agent Framework Learning in multi-agent setting all agents simultaneously learning environment not stationary (other agents are evolving) problem of a “moving target”

13. University of Waterloo Page 13 Stochastic Game Framework for addressing MAL From the perspective of sequential decision making: Markov decision processes one decision maker multiple states Repeated games multiple decision makers one state Stochastic games (Markov games) extension of MDPs to multiple decision makers multiple states

14. University of Waterloo Page 14 Stochastic Game / Notation S : Set of states (n-agent stage games) R i (s,a) : Reward to player i in state s under joint action a T(s,a,s) : Probability of transition from s to state s on a  a1a1 R 1 (s,a), R 2 (s,a), … a2a2  s [ ] s T(s,a,s)T(s,a,s) From dynamic programming approach: Q i (s,a): Long-run payoff to i from s on a then equilibrium

15. September 15September 15 Approach Multiagent learning using a variable learning rate

16. University of Waterloo Page 16 Evaluation criteria for multi-agent learning Use of convergence to NE is problematic: Terminating criterion: Equilibrium identifies conditions under which learning can or should stop Easier to play in equilibrium as opposed to continued computation Nash equilibrium strategy has no “prescriptive force”: say anything prior to termination Multiple potential equilibria Opponent may not wish to play an equilibria Calculating a Nash Equilibrium can be intractable for large games New criteria: rationality and convergence in self-play Converge to stationary policy: not necessarily Nash Only terminates once best response to play of other agents is found During self play, learning is only terminated in a stationary NE

17. University of Waterloo Page 17 Contributions and Assumptions Contributions: Criterion for multi-agent learning algorithms A simple Q-learning algorithm that can play mixed strategies The WoLF PHC (Win or Lose Fast Policy Hill Climber) Assumptions - gets both properties given that: The game is two-player, two-action Players can observe each other’s mixed strategies (not just the played action) Can use infinitesimally small step sizes

18. University of Waterloo Page 18 Opponent Modeling or Joint-Action Learners C. Claus, C. Boutilier, 1998

19. University of Waterloo Page 19 Joint-Action Learners Method Maintains an explicit model of the opponents for each state. Q-values are maintained for all possible joint actions at a given state The key assumption is that the opponent is stationary Thus, the model of the opponent is simply frequencies of actions played in the past Probability of playing action a -i : where C(a −i ) is the number of times the opponent has played action a −i. n(s) is the number of times state s has been visited.

20. University of Waterloo Page 20 Opponent modeling FP-Q learning algorithm

21. University of Waterloo Page 21 WoLF Principles The idea is to use two different strategy update steps, one for winning and another one for loosing situations “Win or Learn Fast”: agent reduces its learning rate when performing well, and increases when doing badly. Improves convergence of IGA and policy hill-climbing To distinguish between those situations, the player keeps track of two policies. Winning is considered if the expected utility of the actual policy is greater than the expected utility of the equilibrium (or average) policy. If winning, the smaller of two strategy update steps is chosen by the winning agent.

22. University of Waterloo Page 22 Incremental Gradient Ascent Learners (IGA) IGA: incrementally climbs on the mixed strategy space for 2-player 2-action general sum games guarantees convergence to a Nash equilibrium or guarantees convergence to an average payoff that is sustained by some Nash equilibrium WoLF IGA: based on WoLF principle converges guarantee to a Nash equilibrium for all 2 player 2 action general sum games

23. University of Waterloo Page 23 Information passing in the PHC algorithm

24. University of Waterloo Page 24 Simple Q-Learner that plays mixed strategies Problems: guarantees rationality against stationary opponents does not converge in self-play Updating a mixed strategy by giving more weight to the action that Q-learning believes is the best

25. University of Waterloo Page 25 WoLF Policy Hill Climbing algorithm Maintaining average policy Determination of “W” and “L”: by comparing the expected value of the current policy to that of the average policy agent only need to see its own payoff converges for two player two action SG’s in self-play Probability of playing action

26. September 15September 15 Theoretical analysis Analysis of the replicator dynamics

27. University of Waterloo Page 27 Replicator Dynamics – Simplification Case Best response dynamics for Paper-Rock-Scissors Circular shift from one agent’s policy to the other’s average reward

28. University of Waterloo Page 28 A winning strategy against PHC If winning play probability 1 for current preferred action in order to maximize rewards while winning If losing play a deceiving policy until we are ready to take advantage of them again 0 1 1 0.5 Probability we play heads Probability opponent plays heads

29. University of Waterloo Page 29 Ideally we’d like to see this: winning losing

30. University of Waterloo Page 30 Ideally we’d like to see this: winning losing

31. University of Waterloo Page 31 Convergence dynamics of strategies Iterated Gradient Ascent: Again does a myopic adaptation to other players’ current strategy. Either converges to a Nash fixed point on the boundary (at least one pure strategy), or get limit cycles Vary learning rates to be optimal while satisfying both properties

32. September 15September 15 Results

33. University of Waterloo Page 33 Experimental testbeds Matrix Games Matching pennies Three-player matching pennies Rock-paper-scissors Gridworld Soccer

34. University of Waterloo Page 34 Matching pennies

35. University of Waterloo Page 35 Rock-paper-scissors: PHC

36. University of Waterloo Page 36 Rock-paper-scissors: WoLF PHC

37. University of Waterloo Page 37 Summary and Conclusion Criterion for multi-agent learning algorithms: rationality and convergence A simple Q-learning algorithm that can play mixed strategies The WoLF PHC (Win or Lose Fast Policy Hill Climber) to satisfy rationality and convergence

38. University of Waterloo Page 38 Disadvantages Analysis for two-player, two-action games: pseudoconvergence Avoidance of exploitation guaranteeing that the learner cannot be deceptively exploited by another agent Chang and Kaelbling (2001) demonstrated that the best- response learner PHC (Bowling & Veloso, 2002) could be exploited by a particular dynamic strategy.

39. University of Waterloo Page 39 Pseudoconvergence

40. University of Waterloo Page 40 Future Work by Authors Exploring learning outside of self-play: whether WoLF techniques can be exploited by a malicious (not rational) “learner”. Scaling to large problems: combining single-agent scaling solutions (function approximators and parameterized policies) with the concepts of a variable learning rate and WoLF. Online learning List other algorithms of authors: GIGA-WoLF, normal form games

41. University of Waterloo Page 41 Discussion / Open Questions Investigation other evaluation criteria: No-regret criteria Negative non-convergence regret (NNR) Fast reaction (tracking) [Jensen] Performance: maximum time for reaching a desired performance level Incorporating more algorithms into testing: deeper comparison with more simple and more complex algorithms (e.g. AWESOME [Conitzer and Sandholm 2003]) Classification of situations (games) with various values of the delta and alpha variables: what values are good in what situations. Extending work to have more players. Online learning and exploration policy in stochastic games (trade-off) Currently the formalism is presented in two dimensional state-space: possibility for extension of the formal model (geometrical ?)? What does make Minimax-Q irrational? Application of WoLF to multi-agent evolutionary algorithms (e.g. to control the mutation rate) or to learning of neural networks (e.g. to determine a winner neuron)? Connection with control theory and learning of Complex Adaptive Systems: manifold-adaptive learning?

42. September 15September 15 Questions Thank you