HyperAI

Atari Games On Atari 2600 Phoenix

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Score

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
Score
Paper TitleRepository
IMPALA (deep)210996.45IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Advantage Learning22038.27Increasing the Action Gap: New Operators for Reinforcement Learning
MuZero955137.84Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
FQF174077.5Fully Parameterized Quantile Function for Distributional Reinforcement Learning
QR-DQN-116585Distributional Reinforcement Learning with Quantile Regression
GDI-I3894460Generalized Data Distribution Iteration-
ASL DDQN71752.6Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity-
R2D2864020.0Recurrent Experience Replay in Distributed Reinforcement Learning-
DNA391085DNA: Proximal Policy Optimization with a Dual Network Architecture
GDI-I3894460GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning-
Prior+Duel hs63597.0Dueling Network Architectures for Deep Reinforcement Learning
IQN56599Implicit Quantile Networks for Distributional Reinforcement Learning
Agent57908264.15Agent57: Outperforming the Atari Human Benchmark
Ape-X224491.1Distributed Prioritized Experience Replay
Persistent AL14495.56Increasing the Action Gap: New Operators for Reinforcement Learning
GDI-H3959580Generalized Data Distribution Iteration-
NoisyNet-Dueling10379Noisy Networks for Exploration
MuZero (Res2 Adam)815728.7Online and Offline Reinforcement Learning by Planning with a Learned Model
DreamerV249375Mastering Atari with Discrete World Models
CGP7520Evolving simple programs for playing Atari games
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