Go-Explore: a New Approach for Hard-Exploration Problems

A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Atari Games Atari 2600 Montezuma's Revenge Go-Explore Score 43763 # 2
Atari Games Atari 2600 Pitfall! Go-Explore Score 107363 # 1

Methods used in the Paper


METHOD TYPE
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