Atari Games On Atari 2600 Pitfall

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		Paper Title
Go-Explore	102571	Go-Explore: a New Approach for Hard-Exploration Problems
Agent57	18756.01	Agent57: Outperforming the Atari Human Benchmark
Go-Explore	6954	First return, then explore
NoisyNet-Dueling	0	Noisy Networks for Exploration
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
ASL DDQN	0	Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
R2D2	0.0	Recurrent Experience Replay in Distributed Reinforcement Learning
GDI-I3	0	GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
GDI-I3	0	Generalized Data Distribution Iteration
Ape-X	-0.6	Distributed Prioritized Experience Replay

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Atari Games On Atari 2600 Pitfall

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