#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

NeurIPS 2017 Haoran TangRein HouthooftDavis FooteAdam StookeXi ChenYan DuanJohn SchulmanFilip De TurckPieter Abbeel

Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Atari Games Atari 2600 Freeway TRPO-hash Score 34.0 # 1
Atari Games Atari 2600 Frostbite TRPO-hash Score 5214.0 # 7
Atari Games Atari 2600 Montezuma's Revenge TRPO-hash Score 75 # 21
Atari Games Atari 2600 Venture TRPO-hash Score 445.0 # 14

Methods used in the Paper


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