Atari Games On Atari 2600 Berzerk

评估指标

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
FQF	12422.2	Fully Parameterized Quantile Function for Distributional Reinforcement Learning	-
A3C FF (1 day) hs	1433.4	Asynchronous Methods for Deep Reinforcement Learning	-
Ape-X	57196.7	Distributed Prioritized Experience Replay	-
IQN	1053	Implicit Quantile Networks for Distributional Reinforcement Learning	-
DDQN+Pop-Art noop	1199.6	Learning values across many orders of magnitude	-
DDQN (tuned) hs	1011.1	Deep Reinforcement Learning with Double Q-learning	-
A3C FF hs	817.9	Asynchronous Methods for Deep Reinforcement Learning	-
A3C LSTM hs	862.2	Asynchronous Methods for Deep Reinforcement Learning	-
ES FF (1 hour) noop	686.0	Evolution Strategies as a Scalable Alternative to Reinforcement Learning	-
Prior noop	1305.6	Prioritized Experience Replay	-
DQN noop	585.6	Deep Reinforcement Learning with Double Q-learning	-
GDI-I3	7607	GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning	-
Prior+Duel hs	2178.6	Deep Reinforcement Learning with Double Q-learning	-
Prior hs	865.9	Prioritized Experience Replay	-
IMPALA (deep)	1852.70	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures	-
C51 noop	1645.0	A Distributional Perspective on Reinforcement Learning	-
Reactor 500M	2303.1	The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning	-
Agent57	61507.83	Agent57: Outperforming the Atari Human Benchmark	-
MuZero	85932.60	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model	-
DQN hs	493.4	Deep Reinforcement Learning with Double Q-learning	-

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