Introduction of "TrailBlazer" algorithm
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論文「Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning」紹介スライドです。NIPS2016読み会@PFN(2017/1/19) https://connpass.com/event/47580/ にて。
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- 1. BLAZING THE TRAILS BEFORE BEATING THE PATH: SAMPLE-EFFICIENT MONTE- CARLO PLANNING KATSUKI OHTO @NIPS2016-YOMI 2017/1/19
- 2. INTRODUCED PAPER • Blazing the trails before beating the path: Sample - efficient Monte-Carlo planning (JB. Grill, M. Valko and R. Munos) • NIPS 2016 accepted paper (poster session) • Abstract starts with “You are a robot…” • http://papers.nips.cc/paper/6253-blazing-the-trails-before- beating-the-path-sample-efficient-monte-carlo-planning
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- 4. AIM • Input : an MDP (Markov Decision Process) (discount factor 𝛾, maximum number of valid actions 𝐾), 𝜀 (> 0), 𝛿 (0 < 𝛿 < 1) • Output : estimated value 𝜇 𝜀,𝛿 of current state 𝑠0 • Aim : Get good estimation of real value 𝒱[𝑠0] of current state such as ℙ 𝜇 𝜀,𝛿 − 𝒱 𝑠0 > 𝜀 ≤ 𝛿 （ ℙ ∙ means probability of ∙ ） with the minimum number of calls to the generative model (state transition function)
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- 6. ALGORITHM OVERVIEW • Global Initialization set 𝜂, 𝜆 as global value set 𝑚 as an argument of root node • Recursive algorithm log(𝜂/𝛾)
- 7. ALGORITHM OVERVIEW 2 • In both MAX nodes and AVG nodes, arguments are 𝑚 (desired branching factor) and 𝜀 (admissible estimation error) • If 𝑚 is large, we can search many children, but we need much time (dilemma) • If 𝜀 is small, we can search deeply, but we need much time (dilemma)
- 8. ALGORITHM FOR AVG NODES • Input : 𝑚 and 𝜀 • Output : estimated value • If admissible error 𝜀 is large, ignore successive reward • Fill 𝑚 transition samples (and store immediate reward) • search all of 𝑚 sampled next states • return averaged immediate reward + estimated successive reward
- 9. ALGORITHM FOR MAX NODES • Input : 𝑚 and 𝜀 • Output : estimated value • Fill candidate action pool ℒ by all valid actions • U is a value like standard error of estimation • Search candidate actions repeatedly until “Only 1 action left” or “Error might be small” • If “Error might be small” then return estimated value of best action else search best action 1 more time carefully
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