This document discusses transfer learning in humans and machines. It covers different types of transfer learning including hierarchical curriculum, multilingualism, and inductive logic programming. It also discusses approaches to transfer learning in reinforcement learning such as starting-point methods, hierarchical methods, and imitation methods. The author's research focuses on skill transfer and macro transfer in reinforcement learning domains like RoboCup soccer. The results show that skill transfer and macro transfer can improve performance on new related tasks.

1. Lisa Torrey University of Wisconsin – Madison CS 540 Transfer Learning

2. Education Hierarchical curriculum Learning tasks share common stimulus-response elements Abstract problem-solving Learning tasks share general underlying principles Multilingualism Knowing one language affects learning in another Transfer can be both positive and negative Transfer Learning in Humans

3. Transfer Learning in AI Given Learn Task T Task S

4. Goals of Transfer Learning higher asymptote higher slope performance higher start training

5. Inductive Learning Search Allowed Hypotheses All Hypotheses

6. Transfer in Inductive Learning Search Allowed Hypotheses All Hypotheses Thrun and Mitchell 1995: Transfer slopes for gradient descent

7. Transfer in Inductive Learning Bayesian methods Bayesian Learning Bayesian Transfer Prior distribution + Data = Posterior Distribution Raina et al.2006: Transfer a Gaussian prior

8. Transfer in Inductive Learning Hierarchical methods Pipe Surface Circle Line Curve Stracuzzi2006: Learn Boolean concepts that can depend on each other

9. Transfer in Inductive Learning Dealing with Missing Data or Labels Task T Task S Shi et al. 2008: Transfer via active learning

10. Reinforcement Learning Agent Q(s1, a) = 0 π(s1) = a1 Q(s1, a1)  Q(s1, a1) + Δ π(s2) = a2 s2 s3 a1 a2 r2 r3 s1 Environment δ(s2, a2) = s3 r(s2, a2) = r3 δ(s1, a1) = s2 r(s1, a1) = r2

11. Transfer in Reinforcement Learning Starting-point methods Hierarchical methods Alteration methods Imitation methods New RL algorithms

12. Transfer in Reinforcement Learning Starting-point methods Initial Q-table transfer Source task no transfer target-task training Taylor et al. 2005: Value-function transfer

13. Transfer in Reinforcement Learning Hierarchical methods Soccer Pass Shoot Run Kick Mehta et al. 2008: Transfer a learned hierarchy

14. Transfer in Reinforcement Learning Alteration methods Task S Original states Original actions Original rewards New states New actions New rewards Walsh et al. 2006: Transfer aggregate states

15. Transfer in Reinforcement Learning New RL Algorithms Agent Q(s1, a) = 0 π(s1) = a1 Q(s1, a1)  Q(s1, a1) + Δ π(s2) = a2 a1 a2 s2 s3 s1 r2 r3 Environment δ(s2, a2) = s3 r(s2, a2) = r3 δ(s1, a1) = s2 r(s1, a1) = r2 Torrey et al. 2006: Transfer advice about skills

16. Transfer in Reinforcement Learning Imitation methods source policy used target training Torrey et al. 2007: Demonstrate a strategy

17. My Research Starting-point methods Hierarchical methods Hierarchical methods Imitation methods New RL algorithms Skill Transfer Macro Transfer

18. RoboCup Domain 3-on-2 KeepAway 3-on-2 BreakAway 2-on-1 BreakAway 3-on-2 MoveDownfield

19. Inductive Logic Programming IF [ ] THEN pass(Teammate) IF distance(Teammate) ≤ 5 THEN pass(Teammate) IF distance(Teammate) ≤ 10 THEN pass(Teammate) … IF distance(Teammate) ≤ 5 angle(Teammate, Opponent) ≥ 15 THEN pass(Teammate) IF distance(Teammate) ≤ 5 angle(Teammate, Opponent) ≥ 30 THEN pass(Teammate)

20. Advice Taking Batch Reinforcement Learning via Support Vector Regression (RL-SVR) Agent Agent Compute Q-functions … Environment Environment Batch 2 Batch 1 Find Q-functions that minimize: ModelSize + C × DataMisfit

21. Advice Taking Batch Reinforcement Learning with Advice (KBKR) Agent Agent Compute Q-functions … Environment Environment Advice Batch 1 Batch 2 + µ × AdviceMisfit Find Q-functions that minimize: ModelSize + C × DataMisfit

22. Skill Transfer Algorithm Source ILP IF distance(Teammate) ≤ 5 angle(Teammate, Opponent) ≥ 30 THEN pass(Teammate) Mapping Advice Taking Target [Human advice]

23. Selected Results Skill transfer to 3-on-2 BreakAway from several tasks

24. Macro-Operators pass(Teammate) move(Direction) IF [ ... ] THEN pass(Teammate) IF [ ... ] THEN move(ahead) IF [ ... ] THEN shoot(goalRight) IF [ ... ] THEN shoot(goalLeft) IF [ ... ] THEN pass(Teammate) IF [ ... ] THEN move(left) IF [ ... ] THEN shoot(goalRight) IF [ ... ] THEN shoot(goalRight) shoot(goalRight) shoot(goalLeft)

25. Demonstration An imitation method source policy used target training

26. Macro Transfer Algorithm Source ILP Demonstration Target

27. Macro Transfer Algorithm Learning structures Positive: BreakAway games that score Negative: BreakAway games that didn’t score ILP IF actionTaken(Game, StateA, pass(Teammate), StateB) actionTaken(Game, StateB, move(Direction), StateC) actionTaken(Game, StateC, shoot(goalRight), StateD) actionTaken(Game, StateD, shoot(goalLeft), StateE) THEN isaGoodGame(Game)

28. Macro Transfer Algorithm Learning rules for arcs Positive: states in good games that took the arc Negative: states in good games that could have taken the arc but didn’t ILP pass(Teammate) shoot(goalRight) IF [ … ] THEN loop(State, Teammate)) IF [ … ] THEN enter(State)

29. Selected Results Macro transfer to 3-on-2 BreakAway from 2-on-1 BreakAway

30. Machine learning is often designed in standalone tasks Transfer is a natural learning ability that we would like to incorporate into machine learners There are some successes, but challenges remain, like avoiding negative transfer and automating mapping Summary