Reinforcement Learningreinforcement-learning
Atari Games
Atari games became the canonical RL benchmark when DeepMind's DQN (2013) learned to play Breakout from raw pixels, but the goalposts keep moving. Agent57 (2020) was the first to achieve superhuman scores on all 57 games, and recent work like BBF and MEME shows that sample efficiency — not just final performance — is the new frontier. The benchmark's age is both its strength (decades of comparable results) and weakness (it doesn't capture the open-ended reasoning modern RL needs).
1
Datasets
16
Results
human-normalized-score
Canonical metric
Canonical Benchmark
Atari 2600
Suite of 57 Atari 2600 games. Standard benchmark for deep reinforcement learning agents.
Primary metric: human-normalized-score
Top 10
Leading models on Atari 2600.
All datasets
1 dataset tracked for this task.
Related tasks
Other tasks in Reinforcement Learning.
Run Inference
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