Atari Games On Atari 2600 Tutankham

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 Title	Repository
DQN hs	45.6	Deep Reinforcement Learning with Double Q-learning
SARSA	98.2	-	-
DDQN (tuned) noop	218.4	Dueling Network Architectures for Deep Reinforcement Learning
Prior noop	204.6	Prioritized Experience Replay
GDI-I3	423.9	Generalized Data Distribution Iteration	-
R2D2	395.3	Recurrent Experience Replay in Distributed Reinforcement Learning	-
MuZero	491.48	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Prior+Duel noop	245.9	Dueling Network Architectures for Deep Reinforcement Learning
NoisyNet-Dueling	269	Noisy Networks for Exploration
CGP	0	Evolving simple programs for playing Atari games
DDQN+Pop-Art noop	183.9	Learning values across many orders of magnitude	-
GDI-I3	423.9	GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning	-
DDQN (tuned) hs	92.2	Deep Reinforcement Learning with Double Q-learning
A3C LSTM hs	144.2	Asynchronous Methods for Deep Reinforcement Learning
GDI-H3	418.2	Generalized Data Distribution Iteration	-
C51 noop	280.0	A Distributional Perspective on Reinforcement Learning
Advantage Learning	245.22	Increasing the Action Gap: New Operators for Reinforcement Learning
DQN noop	68.1	Deep Reinforcement Learning with Double Q-learning
Best Learner	114.3	The Arcade Learning Environment: An Evaluation Platform for General Agents
ASL DDQN	252.9	Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity	-

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