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Atari Games On Atari 2600 Battle Zone

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
SAC4386.7Soft Actor-Critic for Discrete Action Settings-
POP3D15466.67Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization-
Bootstrapped DQN38666.7Deep Exploration via Bootstrapped DQN-
MuZero848623.00Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model-
Duel hs31320.0Dueling Network Architectures for Deep Reinforcement Learning-
IMPALA (deep)20885.00IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures-
C51 noop28742.0A Distributional Perspective on Reinforcement Learning-
A3C FF hs12950.0Asynchronous Methods for Deep Reinforcement Learning-
Prior hs25520.0Prioritized Experience Replay-
R2D2751880.0Recurrent Experience Replay in Distributed Reinforcement Learning-
Duel noop37150.0Dueling Network Architectures for Deep Reinforcement Learning-
DDQN+Pop-Art noop8220.0Learning values across many orders of magnitude-
Ape-X98895Distributed Prioritized Experience Replay-
NoisyNet-Dueling52262Noisy Networks for Exploration-
Agent57934134.88Agent57: Outperforming the Atari Human Benchmark-
DNA71003DNA: Proximal Policy Optimization with a Dual Network Architecture-
DreamerV240325Mastering Atari with Discrete World Models-
CGP34200Evolving simple programs for playing Atari games-
FQF87928.6Fully Parameterized Quantile Function for Distributional Reinforcement Learning-
IQN42244Implicit Quantile Networks for Distributional Reinforcement Learning-
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