Atari Games On Atari 2600 Berzerk

Metrics

Score

Results

Performance results of various models on this benchmark

Model Name	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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