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4 1 1 2 MDP Progressive Widening Double Progressive Widening Weighted UCT Trap Problem Treasure Hunt Problem
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6 1 Markov Decision Process; MDP MDP MDP , 1, 2,... s 0, s 1, s 2,... A 0, A 1, A 2,... R 0, R 1, R 2,... 1
7 1.1: MDP [1] 1.1 MDP s 1 s 0 A 0 R 0 s 0 s 1 A 0,, 1.1 x a r MDP X A P x X a A x r MDP MDP r sum = γ t r t (1.1) γ 0 γ 1 γ < 1 γ = 1 MDP t=0 2
8 2 / MDP
9 2 MDP MDP 2 2 [2] 2 2 [3][4][5] [3][4] Arati2600 RGB [5] 3D MuJoCo 1 end-to-end MRP ; MDP [6] [7] MDP 1 4
10 2.1 1 (policy) / / Adaptive Playout[8] 5
11 [8] [13] UCT [14] 2.3 [9] 1 MDP MDP
12 Thompson Sampling[10] UCB1,UCB1-tuned, UCB2[11] Thompson Sampling UCB1 Thompson Sampling[10] a D A a T S = argmax v a D a (2.1) a A v a D a Thompson Sampling [10] 0 1 UCB1[11] a N a a R a UCB1 v a v a = R a 2 ln a + C A N a UCB (2.2) N a N a a UCB1 = argmax (v a ) (2.3) a A C UCB1 C UCB1 UCB1 optiminizm in the face of uncetrainty; OFU [12] 2.4 [13] [13] ; Expansion 2.1 ; Backpropagation 2.1 ; Selection 7
13 2.1: [13] ; Simulation UCB1 UCT[14] UCT 1 transition(statex, actiona) x a γ UCB1 Algorithm 1 UCT N x,a x a R x,a x a N x x = a A x N x,a function simulationinu CT (x) if x then return x end if a sim argmax a Ax ( Rx,a ) 2 ln N x N x,a N x,a + C UCB x, r transition(x, a sim ) r sim r + γ simulationinuct(x ) N x N x + 1, N x,asim N x,asim + 1, R x,asim R x,asim + r sim x 0 N x0,a 8
14 2.5 UCT 1 MDP GP-UCB[15] [16] UCB1 Weighted UCB (WUCB) [17] k((state, action), (state, action)) WUCB a a 1 a 2 k((s 1, a 1 ), (s 2, a 2 )) = 0 P ( y, b, r) n(x, a) = r(x, a) = y,b,r P y,b,r P k((x, a), (y, b)) (2.4) k((x, a), (y, b)) r (2.5) n(x) = a A n(x, a) (2.6) v a = r(x, a) n(x, a) + C 2 ln n(x) UCB n(x, a) (2.7) a UCB1 = argmax (v a ) (2.8) a A r 2.6 MDP Double Progressive Widening Weighted UCT 4 9
15 2.6.1 Progressive Widening Progressive Widening (PW) A A UCB1 Progressive Widening PW UCT UCT + PW 2 newaction(statex) x α Progressive Widening Algorithm 2 UCT + PW A x x x function simulationinuct P W (x) if x then return x end if if N α x #A x then A x A x newaction(x) end if ( ) R a sim argmax x,a 2 ln a A a A N x,a + C N x,a UCB N x,a x, r transition(x, a sim ) r sim r + γ simulationinuct-pw(x ) N x N x + 1, N x,asim N x,asim + 1, R x,asim R x,asim + r sim PW N x α 0 < α < 1 PW Double Progressive Widening Double Progressive Widening DPW [18] 1 1 DPW PW Double Progressive Widening DPW UCT UCT + DPW DPW [18] UCT DPW 10
16 UCT + DPW DPW DPW 3 [18] 3 PW PW α PW β Algorithm 3 DPW M x,a x a (, ) (x, a) N x,a,(x,r) x a x r function simulationindp W (x) if x then return x end if if N α x #A x then A x A x newaction(x) end if ( ) R a sim argmax x,a 2 ln a A a A N x,a + C N x,a UCB N x,a if N x,a β #X x,a then Mx, a M x,a transition(x, a sim ) end if x, r M x,a N x,a,(x,r) x,r Mx,a N x,a,(x,r) r sim r + γ simulationindpw(x ) N x N x + 1, N x,asim N x,asim + 1, R x,asim R x,asim + r sim, N x,a,(x,r) N x,a,(x,r) + 1 Progressive Widening UCT 2 DPW PW WUCT WUCB 11
17 2.6.3 Weighted UCT DPW DPW WUCB Weighted UCT (WUCT) [17] WUCB UCB1 UCB1 WUCT 4 DPW 4 A Algorithm 4 WUCT P (,, ) function simulationinw U CT (x) if x then return x end if n 0 r 0 for y, a, r P do n[a] n[a] + k((x, a), (y, a)) r[a] r[a] + k((x, a), (y, a)) r end for n num a A N[a] a sim argmax a A ( r[a] n[a] + C UCB ) 2 ln n sum n[a] x, r transition(x, a sim ) r sim r + γ simulationinwuct(x ) P P (x, a sim, r sim ) 1 12
18 DPW [18] WUCT [17] : 13
19 3.1 [0, 1) x x UCT D 1 2 D D 2 D , 5 3.4, a s a N s,a R s,a N s a 1 N s,a R s,a
20 d th(d) d th(d) d d d w(d) w(d) x a ñ(x, a) r(x, a) S x s d(s) s N s,a R s,a a ñ(x, a) = s S w(d(s))n s,a (3.1) r(x, a) = s S w(d(s))r s,a (3.2) x x A ñ(x) = a A ñ(x, a) (3.3) 15
21 s x 0 x 1 x 0 x 1 ñ(x) = s S w(d(s))n s ñ(x, a) r(x, a) x a UCB w(d) ñ(x, a) x UCT f(n) n(x) = f(ñ(x)) (3.4) ñ(x, a) r(x, a) UCB1 n(x, a) r(x, a) n(x, a) = ñ(x, a) n(x) ñ(x) r(x, a) = r(x, a) n(x) ñ(x) (3.5) (3.6) n(x) n(x, a) r(x, a) UCB1 [11] a try a try = argmax a A ( r(x, a) n(x, a) + C UCB C UCB UCB1 ) 2 ln n(x) n(x, a) (3.7)
22 Algorithm 5 N s,a s a R s,a s a N s s nextstate(state, action) w(depth) th(depth) f(frequency) N prior, R prior x function simulation(x) if x then return x end if A x ñ #A N prior r #A R prior s x, r sim simulationonstatet ree(x, 0, s, A, ñ, r) return r sim function simulationonstatet ree(x, d, s, A, ñ, r) W w(d) for i 0 to #A 1 do a A[i] ñ[i] ñ[i] + W N s,a, r[i] r[i] + W R s,a end for if N s th(d) s x then x s s end if if s x then ñ sum summation of ñ n sum f(ñ sum ) n #A i ñ[i] n sum /ñ sum r #A i r[i] n sum /ñ sum a sim A[argmax i 0..#A 1 ( r[i] n[i] + C UCB 2 ln n sum n[i] x, r transition(x, a) r sim r + γ simulation(x ) else s x s a sim, r sim simulationonstatet ree(x, d + 1, s, A, ñ, r) end if N s,asim N s,asim + 1, R s,asim R s,asim + r sim N s N s + 1 return a sim, r sim 17 ) ]
23 Algorithm 6 N s,a s a R s,a s a nextstate(state, action) w(depth) th(depth) f(frequency) N prior, R prior x function simulation(x) if x then return x end if A x ñ #A N prior r #A R prior s x a, r sim simulationonstatet ree(x, 0, s, A, ñ, r) return r sim function simulationonstatet ree(x, d, s, A, ñ, r) W w(d) for i 0 to #A 1 do a A[i] ñ[i] ñ[i] + W N s,a, r[i] r[i] + W R s,a end for if N s,a th(d) s x then x s s end if if s x then ñ sum summation of ñ n sum f(ñ sum ) n #A i ñ[i] n sum /ñ sum r #A i r[i] n sum /ñ sum a sim A[argmax i 0..#A 1 ( r[i] n[i] + C UCB 2 ln n sum n[i] x, r transition(x, a) r sim r + γ simulation(x ) else s x s a sim, r sim simulationonstatet ree(x, d + 1, s, A, ñ, r) end if N s,asim N s,asim + 1, R s,asim R s,asim + r sim return a sim, r sim 18 ) ]
24 Algorithm 7 rootaction(state) (ex. UCB1) x root while do a root rootaction(x root ) x root, r root transition(x root, a root ) r sim r root + γ simulation(x root) r sim a root end while return WUCB WUCT,, n #A O(#An) n O(n 2 ) O(#A ) 1. th(d) O(n) 0 MDP WUCT 2. th(d) d th(d) d d 1 th(d) 19
25 n O(logn) th(d) WUCT O(n) 1 O(n) 3.4 O(1) O(#A) D d O(Dd) O(1) O(Dd) W UCT 20
26 4 [18][19] [18][19] 2 γ = Trap Problem Trap Problem [18] [18] x x 0 = 0 t a t 8 0 i < 8 a i = i [18] [0, 1] [0, R) d x x = x + a + d r x x < x l x < h 0 t t = 0 t = 2 R = 0.01 R = 0.1 [18] R R = 0.01 R =
27 UCT [18] 1.7 t = 2 x 1.7 t = 0, t = 1 a 1 x < t = 1 t = UCT trap DPW WUCT DPW PW 8 PW DPW C UCB = 1 α = 0.3 β = 0.25 WUCT C UCB = 1 4 (,, ) P P i x, a P i x i, a i σ i,a = 0.5 (1 + k((x, a), (x j, a j ))) 1 2 j 0..i 1 (4.1) (x x i ) 2 2σ i,a 2 k((x, a), (x i, a i )) = e 2 (4.2) πσ i,a σ i,a 0.5 O(#P) 4 n[a] 22
28 C UCB = 1 th(d) = d w(d) = (d + 1) 2 f(n sum ) = ñ 2 3 sum : [ 2R, 2 + 2R] 6 s (s, a) WUCT 1 t = 0 t = 0 UCB1 UCB1 C UCB = 1 Intel Corei7-4790K 4.00 GHz WUCT R = 0.01 R = 1 1, 10, 100, 1000, , 10, 100, 1000ms 4 1ms 100ms ms
29 R = 0.01 R = WUCT DPW WUCT WUCT 10ms 1000ms DPW R = 0.01 R = 0.1 WUCT DPW DPW 4.2 Treasure Hunt Problem Treasure Hunt Problem [19] [19] (x, y) Start (0, 0) Treasure D 0 x 0 0 y D G D G x D D G y D R treasure h D H x D+H D H y D+H R hole (x, y) A θ A 4.3 ϵ R time t t = T [19] t (x, y ) x t+1 = max(0, min(x, D)) y t+1 = max(0, min(y, D)) 24
30 (0, 0), (0, D), (D, 0) D = 5 D = 15 G = 1 h = D/2 A = 1 ϵ = 1 R treasure = 1, R hole = 0.5, R time = T = D 10 [19] D = 5 D = WUCT DPW WUCT PW DPW WUCT PW PW α = 0.2 DPW C UCB Treasure Hunt Problem Trap Problem C = 0.3 WUCT C UCB 1 WUCT Trap Problem Trap Problem 4.2 (x x i ) 2 (x x i ) 2 + (y y i ) 2 2 D 25
31 4.1 σ i,a Trap Problem 0.5 D 2 ([0, D], [0, D]) Trap Problem WUCT 10 WUCT WUCT 1 WUCT/ WUCT/ 1 DPW t = 1 WUCT WUCT D = 5 D = 15 1, 10, 100, 1000, , 10, 100, 1000ms 4 1ms, 10ms ms 1000ms 1000 D = 15 1, 10, 100, 1000, , 10, 100, 1000ms 4 1ms 100ms ms 1000 WUCT
32 D = 5 D = D = 5 D = D = 15 DPW D = 5 D = DPW 1ms 10ms WUCT D = 5 D = 15 D = 5 DPW DPW D = 15 1ms WUCT 1 1ms WUCT D = 15 WUCT 1 DPW ms 27
33 4.1: Trap Problem 28
34 4.2: Trap Problem 29
35 4.3: Treasure Hunt Problem [19] 30
36 4.4: Treasure Hunt Problem 31
37 4.5: Treasure Hunt Problem 32
38 4.6: Treasure Hunt Problem 4.7: Treasure Hunt Problem 33
39 MDP [21][22] 34
![5.1: 1 Box2D 1 [23]](/docs-images/91/106903069/images/40-0.jpg "1 http://box2d.")
40 5.1: 1 Box2D 1 [23]
41 5.3 [24] [25] expectimax [26] Yee [27] kernel regression UCT (KR-UCT) KR-UCT end-to-end 5.4 MDP x a v x, v y w
42 : x y 1 x y 15 2 [29] 15 d d x y mm 15.8m 37
43 d th(d) th(d) = 1.3 d (5.1) w(d) w(d) = (d + 1) 4 (5.2) w(d) x ñ(x) n(x) f(n sum ) = ñ 0.4 sum (5.3) UCB1 C UCB = N s,a, R s,a, N s w(depth), th(depth), f(frequency) 5 γ 1 transition(state, action) 5 1 N new 2 l 2l 38
44 Algorithm 8 A s s R s s N new N policy function simulationincurling(x) if x then return x end if A x x ñ 0 r 0 s x, r sim simulationonstatet ree(x, 0, s, A x, ñ, r) return r sim function simulationonstatet reeincurling(x, d, s, A x, ñ, r) W w(d) for a A x do if a A s then ñ[a] ñ[a] + W N s,a, r[a] r[a] + W R s,a else ñ[a] ñ[a] + W N new, r[a] r[a] + R s /N s end if end for if N s th(d) s x then x s s N s 0 end if if s x then ñ sum summation of ñ n sum f(ñ sum ) n A x a ñ[a] n sum /ñ sum r A x a r[a] n sum /ñ sum n sum n sum + N policy #A ( x a sim A[argmax r[i] + C i 0..#A 1 n[i] UCB ) 2 ln n sum ] n[i] r sim simulationincurling(transition(x, a sim )) else s x s a sim, r sim simulationonstatet reeincurling(x, d + 1, s, A, ñ, r) end if if s N s,asim, s,asim then N s,asim N new, R s,asim 0, N s N s + N new end if 39 N s,asim N s,asim + 1, R s,asim R s,asim + r sim N s N s + 1, R s R s + r sim return a sim, r sim
45 2 N new 3 n sum N policy #A x 2 r[a] R s /N s W 3 n(x, a) r(x, a) N policy n sum [24] l ( l 2, 2l) ( l 4, ) [24] URL 40
![5.2: [24] 5.2 Q p Q f 1440 2142 [24] 5.](/docs-images/91/106903069/images/46-0.jpg "2 3 2 [24][25] [25] 2142 1440 2880 UCB")
46 5.2: [24] 5.2 Q p Q f [24] [24][25] [25] UCB 41
47 1 9 transitionw ithoutnoise(state, action) Algorithm 9 allrootactions(state) candidaterootactions(state) C limit c 0 A all allrootactions(x) N root #A all 0 R root #A all 0 while c < C limit do seq 0 #A all 1 for i 0 to #A all 1 do x transitionw ithoutnoise(x, A all [seq[i]]) N root [seq[i]] N root [seq[i]] + 1 R root [seq[i]] R root [seq[i]] + simulationincurling(x) c c + 1 end for end while A cand candidaterootactions(x) for a A cand do end for UCB1[11] [28] 42
48 Box2D GAT GAT (2016) 43
49 : (2 ) (p = ) 5.3: (10 ) (p = )
50 6 WUCT DPW 2 4 Trap Problem WUCT Treasure Hunt Problem Trap Problem Treasure Hunt Problem Trap Problem WUCT Treasure Hunt Problem WUCT Treasure Hunt Problem DPW Trap Problem DPW RAVE DPW-RAVE[30] D = 5 Treasure Hunt Problem DPW [30] DPW-RAVE Backpropagation
51 2 3 Treasure Hunt Problem D = 15 46
52 47
53 [1] Poole, D., Mackworth, A.: Artificial Intelligence, ( ) [2] Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabis, D.: Mastering the game of Go with deep neural networks and tree search, Nature, Vol. 529, pp (2016) [3] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, Martin., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning, Nature, Vol. 518, (2015) [4] Mnih, V., Badia, A. P., Mirza, M., Graves, A., Harley, T., Lillicrap, T. P., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning, pp (2016) [5] Wang, Z., Bapst, V., Heess, N., Mnih, V., Munos, R., Kavukcuoglu, K., de Freitas, N.: Sample efficient actor critic with exprtience replay. ICLR 2017 (2017) [6] Silver, D., van Hasselt, H., Hessel, M., Schaul, T., Guez, A., Harley, T., Dulac- Arnold, G., Reichert, D., Rabinowitz, N., Barreto, A., Degris, T.: The predictron: End-to-end learning and planning, (2017) [7] Genesereth, M., Thielscher, M.: General game playing. Synthesis Lectures on Artificial Intelligence and Machine Learning, 8(2), pp (2014) [8] Graf, T., Platzer, M.: Adaptive Playouts in Monte Carlo Tree Search with Policy Gradient Reinforcement Learning, Advances in Computer Games. Lecture Notes in Computer Science, vol Springer, Cham (2015) [9] Robbins, H.: Some aspects of the sequential design of experiments, Bull. Amer. Math. Soc. 58, No. 5, pp (1952) [10] Thompson, W. R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples, Biometrika, 25 (3-4) pp (1933) 48
54 [11] Auer, P., Cesa-Bianchi N., Fischer, P.: Finite-time Analysis of the Multiarmed Bandit Problem. Machine Learning, Vol. 47, pp (2002) [12] Lai, T. L., and Robbins, H. Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, Vol. 6, pp (1985) [13] Browne, C., Powley, E., Whitehouse, D., Lucas, S., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 4, No. 1, pp (2012) [14] Kocsis, L., Szepesvari, C.: Bandit based Monte-Carlo Planning. European conference on machine learning (ECML2006), pp (2006) [15] Srinivas, N., Krause, A., Kakade, S. M., Seeger, M.: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp (2010) [16] Krause, A., Ong, C. S.: Contextual Gaussian Process Bandit Optimization, Advances in Neural Information Processing Systems, pp (2011) [17] Weinstein, A.: Local Planning for Continuous Markov Decision Processes. Ph. D. thesis (2014) [18] Couetoux, A., Hoock, J., Sokolovska, N., Teytaud, O., Bonnard, N.: Continuous upper confidence trees. International Conference on Learning and Intelligent Optimization, pp (2011) [19] Couetoux, A.: Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems. doctoral thesis, Paris university (2013) [20] Chaslot. G., Fiter, C., Hoock, J.P., Rimmel A., Teytaud, O.: Adding expert knowledge and exploration in Monte-Carlo Tree Search, Advances in Computer Games, LNCS, Vol. 6048, pp (2009) [21],, :,, 2014-GI-31, No. 2, pp. 1-5 (2014) [22] Ito, T., Kitasei, Y.: Proposal and Implementation of Digital Curling, 2015 IEEE Conference on Computational Intelligence and Games, pp (2015) [23], : 2015,, 2016-GI-36, No. 2, pp. 1-6 (2016) [24],, :,, Vol. 57, No. 11, pp (2016) 49
55 [25] Yamamoto, M., Kato, S., Iizuka, H.: Digital Curling Strategy on Game Tree Search, 2015 IEEE Conference on Computational Intelligence and Games, pp (2015) [26], : AI pp (2016) [27] Yee, T., Lisy, V., Bowling, M.: Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty. International Joint Conference on Artificial Intelligence (2016) [28] Audibert, J. Y., Munos, R., and Szepesvari, C.: Exploration-exploitation tradeoff using variance estimates in multi-armed bandits, Theoretical Computer Science, 410(19) pp (2009) [29] Zobrist, A,: A New Hashing Method with Applications for Game Playing. ICCA journal, Vol. 13, No. 2, pp (1970) [30] Couetoux, A., Milone, M., Brendel, M., Doghmen, H., Sebag, M., Teytaud, O.: Continuous rapid action value estimates. The 3rd Asian Conference on Machine Learning, Vol. 20, pp (2011) 50
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A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi
A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi ODA Department of Human and Mechanical Systems Engineering,
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1 Takanobu Mizuta 2 Kiyoshi Izumi 1 SPARX Asset Management Co., Ltd. 2 School of Engineering, The University of Tokyo 1. 2000 2010 1 () ( ) [Farmer 12, Budish 15] [Budish 15] ( ) [Budish 15] : mizutata@gmail.com
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JAIST Reposi https://dspace.j Title ゲームの主目的達成を意図しない人間らしい行動の分 類と模倣 Author(s) 中川, 絢太 Citation Issue Date 2017-03 Type Thesis or Dissertation Text version author URL http://hdl.handle.net/10119/14157 Rights
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1 2 3 A projection-based method for interactive 3D visualization of complex graphs Masanori Takami, 1 Hiroshi Hosobe 2 and Ken Wakita 3 Proposed is a new interaction technique to manipulate graph layouts