Timezone: »
Poster
Online Planning with Lookahead Policies
Yonathan Efroni · Mohammad Ghavamzadeh · Shie Mannor
Real Time Dynamic Programming (RTDP) is an online algorithm based on Dynamic Programming (DP) that acts by 1-step greedy planning. Unlike DP, RTDP does not require access to the entire state space, i.e., it explicitly handles the exploration. This fact makes RTDP particularly appealing when the state space is large and it is not possible to update all states simultaneously. In this we devise a multi-step greedy RTDP algorithm, which we call $h$-RTDP, that replaces the 1-step greedy policy with a $h$-step lookahead policy. We analyze $h$-RTDP in its exact form and establish that increasing the lookahead horizon, $h$, results in an improved sample complexity, with the cost of additional computations. This is the first work that proves improved sample complexity as a result of {\em increasing} the lookahead horizon in online planning. We then analyze the performance of $h$-RTDP in three approximate settings: approximate model, approximate value updates, and approximate state representation. For these cases, we prove that the asymptotic performance of $h$-RTDP remains the same as that of a corresponding approximate DP algorithm, the best one can hope for without further assumptions on the approximation errors.
Author Information
Yonathan Efroni (Microsoft Research, New York)
Mohammad Ghavamzadeh (Google Research)
Shie Mannor (Technion)
More from the Same Authors
-
2020 Workshop: The Challenges of Real World Reinforcement Learning »
Daniel Mankowitz · Gabriel Dulac-Arnold · Shie Mannor · Omer Gottesman · Anusha Nagabandi · Doina Precup · Timothy A Mann · Gabriel Dulac-Arnold -
2020 Session: Orals & Spotlights Track 09: Reinforcement Learning »
Pulkit Agrawal · Mohammad Ghavamzadeh -
2019 Poster: Distributional Policy Optimization: An Alternative Approach for Continuous Control »
Chen Tessler · Guy Tennenholtz · Shie Mannor -
2019 Poster: Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning »
Chao Qu · Shie Mannor · Huan Xu · Yuan Qi · Le Song · Junwu Xiong -
2019 Poster: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor -
2019 Spotlight: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor -
2018 Poster: Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning »
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor -
2018 Spotlight: Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning »
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor -
2018 Poster: Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning »
Tom Zahavy · Matan Haroush · Nadav Merlis · Daniel J Mankowitz · Shie Mannor -
2017 Workshop: Hierarchical Reinforcement Learning »
Andrew G Barto · Doina Precup · Shie Mannor · Tom Schaul · Roy Fox · Carlos Florensa