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Atari Games On Atari 2600 Kung Fu Master

Metrics

Score

Results

Performance results of various models on this benchmark

Model Name
Score
Paper TitleRepository
Prior noop39581.0Prioritized Experience Replay-
DQN hs20882.0Deep Reinforcement Learning with Double Q-learning-
DDQN (tuned) noop29710.0Dueling Network Architectures for Deep Reinforcement Learning-
A3C LSTM hs40835.0Asynchronous Methods for Deep Reinforcement Learning-
Advantage Learning32182.99Increasing the Action Gap: New Operators for Reinforcement Learning-
Prior+Duel noop48375.0Dueling Network Architectures for Deep Reinforcement Learning-
A3C FF (1 day) hs3046.0Asynchronous Methods for Deep Reinforcement Learning-
GDI-I3140440Generalized Data Distribution Iteration-
FQF111138.5Fully Parameterized Quantile Function for Distributional Reinforcement Learning-
DDQN (tuned) hs30207.0Deep Reinforcement Learning with Double Q-learning-
Bootstrapped DQN36733.3Deep Exploration via Bootstrapped DQN-
DDQN+Pop-Art noop34393.0Learning values across many orders of magnitude-
CGP57400Evolving simple programs for playing Atari games-
POP3D33728Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization-
Persistent AL34650.91Increasing the Action Gap: New Operators for Reinforcement Learning-
GDI-H3 (200M)1666000GDI: Rethinking What Makes Reinforcement Learning Different from Supervised Learning-
DreamerV262741Mastering Atari with Discrete World Models-
NoisyNet-Dueling41672Noisy Networks for Exploration-
Gorila20620.0Massively Parallel Methods for Deep Reinforcement Learning-
DQN noop26059.0Deep Reinforcement Learning with Double Q-learning-
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