Atari Games On Atari 2600 Pitfall

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이 벤치마크에서 각 모델의 성능 결과

모델 이름	Score	Paper Title	Repository
NoisyNet-Dueling	0	Noisy Networks for Exploration
Go-Explore	6954	First return, then explore
POP3D	0	Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization
QR-DQN-1	0	Distributional Reinforcement Learning with Quantile Regression
IQN	0	Implicit Quantile Networks for Distributional Reinforcement Learning
DNA	0	DNA: Proximal Policy Optimization with a Dual Network Architecture
Advantage Learning	0	Increasing the Action Gap: New Operators for Reinforcement Learning
MuZero (Res2 Adam)	0	Online and Offline Reinforcement Learning by Planning with a Learned Model
SND-V	0	Self-supervised network distillation: an effective approach to exploration in sparse reward environments
MuZero	0.00	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
SND-VIC	0	Self-supervised network distillation: an effective approach to exploration in sparse reward environments
DreamerV2	0	Mastering Atari with Discrete World Models
CGP	0	Evolving simple programs for playing Atari games
GDI-H3	-4.345	Generalized Data Distribution Iteration	-
Ape-X	-0.6	Distributed Prioritized Experience Replay
Go-Explore	102571	Go-Explore: a New Approach for Hard-Exploration Problems
ASL DDQN	0	Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity	-
IMPALA (deep)	-1.66	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
R2D2	0.0	Recurrent Experience Replay in Distributed Reinforcement Learning	-
RND	-3	Exploration by Random Network Distillation

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