Chapter 7: Eligibility Traces. R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1

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1 Chapter 7: Eligibility Traces R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1

Midterm Mean = 77.33 Median = 82 R. S. Sutton and A.

2 Midterm Mean = Median = 82 R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 2

3 N-step TD Prediction Idea: Look farther into the future when you do TD backup (1, 2, 3,, n steps) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 3

4 Mathematics of N-step TD Prediction Monte Carlo: R t = r " 2 t+ 1 + " rt " rt L+ T! t! 1 r T TD: R! (1) t = rt Vt t+ ( s 1) Use V to estimate remaining return n-step TD: 2 step return: R! (2) t 2 = rt + 1 +! rt Vt t+ ( s 2) n-step return: ( n) 2 n! 1 n R t = rt " rt " rt L+ " rt + n + " Vt ( st+ n ) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 4

5 Learning with N-step Backups Backup (on-line or off-line):!v t (s t ) = "[ R (n) t # V t (s t )] Error reduction property of n-step returns max s n! n E! { Rt st = s} # V ( s) $ " maxv ( s) # V s! ( s) n step return Maximum error using n-step return Maximum error using V Using this, you can show that n-step methods converge R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 5

6 Random Walk Examples How does 2-step TD work here? How about 3-step TD? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 6

7 A Larger Example Task: 19 state random walk Do you think there is an optimal n (for everything)? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 7

8 Averaging N-step Returns n-step methods were introduced to help with TD(λ) understanding Idea: backup an average of several returns e.g. backup half of 2-step and half of 4- step avg 1 (2) R t = Rt R (4) t One backup Called a complex backup Draw each component Label with the weights for that component R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 8

9 Forward View of TD(λ) TD(λ) is a method for averaging all n-step backups weight by λ n-1 (time since visitation) λ-return: R! t = (1"!) $! n "1 (n) R t n=1 Backup using λ-return: #!V t (s t ) = "[ R # t $ V t (s t )] R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 9

10 λ-return Weighting Function R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 10

11 Relation to TD(0) and MC λ-return can be rewritten as: T " t "1 R! t = (1"!) #! n"1 R (n) t +! T "t "1 R t n=1 Until termination If λ = 1, you get MC: After termination T "t "1 R! t = (1"1) # 1 n"1 (n R ) t + 1 T " t "1 R t = R t n=1 If λ = 0, you get TD(0) T "t "1 R! t = (1" 0) # 0 n"1 (n R ) t + 0 T " t "1 (1) R t = R t n=1 R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 11

12 Forward View of TD(λ) II Look forward from each state to determine update from future states and rewards: R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 12

13 λ-return on the Random Walk Same 19 state random walk as before Why do you think intermediate values of λ are best? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 13

14 Backward View of TD(λ) The forward view was for theory The backward view is for mechanism + New variable called eligibility trace e t (s)"! On each step, decay all traces by γλ and increment the trace for the current state by 1 Accumulating trace %!"e t #1 (s) e t (s) = & '!"e t #1 (s) +1 if s $ s t if s = s t R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 14

15 On-line Tabular TD(λ) Initialize V(s) arbitrarily and e(s) = 0, for all s!s Repeat (for each episode) : Initialize s Repeat (for each step of episode) : a " action given by # for s Take action a, observe reward, r, and next state s $ % " r +&V( s $ ) ' V (s) e(s) " e(s) +1 For all s : s " s $ V(s) " V(s) +(%e(s) e(s) " &)e(s) Until s is terminal R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 15

16 Backward View # t = r t+ 1 + " Vt ( st+ 1)! Vt ( st ) Shout δ t backwards over time The strength of your voice decreases with temporal distance by γλ R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 16

17 Relation of Backwards View to MC & TD(0) Using update rule: As before, if you set λ to 0, you get to TD(0) If you set λ to 1, you get MC but in a better way # V ( s) = e ( s) t!" t Can apply TD(1) to continuing tasks t Works incrementally and on-line (instead of waiting to the end of the episode) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 17

18 Forward View = Backward View The forward (theoretical) view of TD(λ) is equivalent to the backward (mechanistic) view for off-line updating The book shows: T "1 #!V TD t (s) = #!V $ t (s t ) I sst t = 0 T "1 t = 0 T "1 #!V TD t (s) = # $ I sst #(%&) k " t ' k $!V " t (s t )I sst = $ % I sst $ (&") k # t ' k t = 0 T "1 t = 0 Backward updates Forward updates algebra shown in book T "1 k =t T #1 On-line updating with small α is similar t = 0 T #1 t = 0 T #1 k =t R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 18

19 On-line versus Off-line on Random Walk Same 19 state random walk On-line performs better over a broader range of parameters R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 19

20 Control: Sarsa(λ) Save eligibility for state-action pairs instead of just states $ e t (s, a) =!"e t #1(s, a) +1 % &!"e t #1 (s,a) if s = s t and a = a t otherwise Q t +1 (s, a) = Q t (s, a) +'( t e t (s, a) ( t = r t +1 +!Q t (s t +1,a t +1 ) # Q t (s t, a t ) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 20

21 Sarsa(λ) Algorithm Initialize Q(s,a) arbitrarily and e(s, a) = 0, for all s, a Repeat (for each episode) : Initialize s, a Repeat (for each step of episode) : Take action a, observe r, s! Choose a! from s! using policy derived from Q (e.g.? - greedy) " # r +$Q( s!, a!) % Q(s, a) e(s,a) # e(s,a) +1 For all s,a : Q(s, a) # Q(s, a) +&"e(s, a) e(s, a) # $'e(s, a) s # s!;a # a! Until s is terminal R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 21

22 Sarsa(λ) Gridworld Example With one trial, the agent has much more information about how to get to the goal not necessarily the best way Can considerably accelerate learning R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 22

23 Three Approaches to Q(λ) How can we extend this to Q- learning? If you mark every state action pair as eligible, you backup over non-greedy policy Watkins: Zero out eligibility trace after a nongreedy action. Do max when backing up at first non-greedy choice. e t (s, a) = % 1 +!"e t #1 (s, a) ' & 0 ( '!"e t #1 (s,a) if s = s t, a = a t,q t #1 (s t,a t ) = max a Q t #1 (s t, a) if Q t #1 (s t,a t ) $ max a Q t #1 (s t,a) otherwise Q t +1 (s, a) = Q t (s, a) +)* t e t (s, a) * t = r t +1 +! max a + Q t (s t +1, a + ) # Q t (s t,a t ) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 23

24 Watkins s Q(λ) Initialize Q(s,a) arbitrarily and e(s, a) = 0, for all s, a Repeat (for each episode) : Initialize s, a Repeat (for each step of episode) : Take action a, observe r, s! Choose a! from s! using policy derived from Q (e.g.? - greedy) a * " arg max b Q( s!, b) (if a ties for the max, then a * " a!) # " r +$Q( s!, a!) % Q(s, a * ) e(s,a) " e(s,a) +1 For all s,a : Q(s, a) " Q(s, a) +&#e(s, a) If a! = a *, then e(s, a) "$'e(s,a) s " s!;a " a! Until s is terminal else e(s, a) " 0 R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 24

25 Peng s Q(λ) Disadvantage to Watkins s method: Peng: Early in learning, the eligibility trace will be cut (zeroed out) frequently resulting in little advantage to traces Backup max action except at end Never cut traces Disadvantage: Complicated to implement R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 25

26 Naïve Q(λ) Idea: is it really a problem to backup exploratory actions? Never zero traces Always backup max at current action (unlike Peng or Watkins s) Is this truly naïve? Works well is preliminary empirical studies What is the backup diagram? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 26

27 Comparison Task Compared Watkins s, Peng s, and Naïve (called McGovern s here) Q(λ) on several tasks. See McGovern and Sutton (1997). Towards a Better Q( λ) for other tasks and results (stochastic tasks, continuing tasks, etc) Deterministic gridworld with obstacles 10x10 gridworld 25 randomly generated obstacles 30 runs α = 0.05, γ = 0.9, λ = 0.9, ε = 0.05, accumulating traces From McGovern and Sutton (1997). Towards a better Q(λ) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 27

28 Comparison Results From McGovern and Sutton (1997). Towards a better Q(λ) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 28

29 Convergence of the Q(λ) s None of the methods are proven to converge. Much extra credit if you can prove any of them. Watkins s is thought to converge to Q * Peng s is thought to converge to a mixture of Q π and Q * Naïve - Q *? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 29

30 Eligibility Traces for Actor-Critic Methods Critic: On-policy learning of V π. Use TD(λ) as described before. Actor: Needs eligibility traces for each state-action pair. We change the update equation: # p t +1 (s, a) = p t(s,a) +!" t if a = a t and s = s t $ % p t (s, a) otherwise Can change the other actor-critic update: to pt + 1( s, a) = pt ( s, a) +!" tet ( s, a) [ ] if a = a t and s = s t % p t +1 (s, a) = p t(s,a) +!" t 1# $(s, a) & ' p t (s,a) otherwise to pt + 1( s, a) = pt ( s, a) +!" tet ( s, a) where % e t (s, a) =!"e (s, a) +1 # $ (s,a ) t #1 t t t & '!"e t #1 (s, a) if s = s and a = a t t otherwise R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 30

31 Replacing Traces Using accumulating traces, frequently visited states can have eligibilities greater than 1 This can be a problem for convergence Replacing traces: Instead of adding 1 when you visit a state, set that trace to 1 % e t (s) =!"e t #1(s) if s $ s t & ' 1 if s = s t R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 31

32 Replacing Traces Example Same 19 state random walk task as before Replacing traces perform better than accumulating traces over more values of λ R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 32

33 Why Replacing Traces? Replacing traces can significantly speed learning They can make the system perform well for a broader set of parameters Accumulating traces can do poorly on certain types of tasks Why is this task particularly onerous for accumulating traces? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 33

34 More Replacing Traces Off-line replacing trace TD(1) is identical to first-visit MC Extension to action-values: When you revisit a state, what should you do with the traces for the other actions? Singh and Sutton say to set them to zero: e t (s, a) = $ & 1 if s = s t and a = a t % 0 if s = s t and a ( a t & '!"e t #1 (s, a) if s ( s t R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 34

35 Implementation Issues Could require much more computation But most eligibility traces are VERY close to zero If you implement it in Matlab, backup is only one line of code and is very fast (Matlab is optimized for matrices) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 35

36 Variable λ Can generalize to variable λ e t (s) = %!" t e t #1 (s) if s $ s t & '!" t e t #1 (s) +1 if s = s t Here λ is a function of time Could define " t = "( s t ) or " t = " t! R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 36

37 Conclusions Provides efficient, incremental way to combine MC and TD Includes advantages of MC (can deal with lack of Markov property) Includes advantages of TD (using TD error, bootstrapping) Can significantly speed learning Does have a cost in computation R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 37

38 Something Here is Not Like the Other R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 38

The Book: Where we are and where we re going. CSE 190: Reinforcement Learning: An Introduction. Chapter 7: Eligibility Traces. Simple Monte Carlo

The Book: Where we are and where we re going. CSE 190: Reinforcement Learning: An Introduction. Chapter 7: Eligibility Traces. Simple Monte Carlo CSE 190: Reinforcement Learning: An Introduction Chapter 7: Eligibility races Acknowledgment: A good number of these slides are cribbed from Rich Sutton he Book: Where we are and where we re going Part