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Atari Games On Atari 2600 Crazy Climber

评估指标

Score

评测结果

各个模型在此基准测试上的表现结果

模型名称
Score
Paper TitleRepository
C51 noop179877.0A Distributional Perspective on Reinforcement Learning-
A3C FF (1 day) hs101624.0Asynchronous Methods for Deep Reinforcement Learning-
Prior noop141161.0Prioritized Experience Replay-
GDI-I3201000GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning-
GDI-I3201000Generalized Data Distribution Iteration-
IMPALA (deep)136950.00IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures-
Reactor 500M236422.0The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning-
DDQN+Pop-Art noop119679.0Learning values across many orders of magnitude-
R2D2366690.7Recurrent Experience Replay in Distributed Reinforcement Learning-
DreamerV2161839Mastering Atari with Discrete World Models-
Duel noop143570.0Dueling Network Architectures for Deep Reinforcement Learning-
DDQN (tuned) noop117282.0Dueling Network Architectures for Deep Reinforcement Learning-
IQN179082Implicit Quantile Networks for Distributional Reinforcement Learning-
FQF223470.6Fully Parameterized Quantile Function for Distributional Reinforcement Learning-
A3C FF hs112646.0Asynchronous Methods for Deep Reinforcement Learning-
ASL DDQN166019Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity-
Ape-X320426Distributed Prioritized Experience Replay-
CGP12900Evolving simple programs for playing Atari games-
Bootstrapped DQN137925.9Deep Exploration via Bootstrapped DQN-
Agent57565909.85Agent57: Outperforming the Atari Human Benchmark-
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