Atari Games On Atari 2600 Tennis

Metrics

Score

Results

Performance results of various models on this benchmark

Model Name	Score	Paper Title	Repository
SARSA	0.0	-	-
Duel noop	5.1	Dueling Network Architectures for Deep Reinforcement Learning	-
ASL DDQN	22.3	Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity	-
Recurrent Rational DQN Average	20.6	Adaptive Rational Activations to Boost Deep Reinforcement Learning	-
POP3D	-8.32	Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization	-
R2D2	-0.1	Recurrent Experience Replay in Distributed Reinforcement Learning	-
Rational DQN Average	20.5	Adaptive Rational Activations to Boost Deep Reinforcement Learning	-
GDI-I3	24	GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning	-
Nature DQN	-2.5	Human level control through deep reinforcement learning
Agent57	23.84	Agent57: Outperforming the Atari Human Benchmark	-
DQN noop	12.2	Deep Reinforcement Learning with Double Q-learning	-
IQN	23.6	Implicit Quantile Networks for Distributional Reinforcement Learning	-
UCT	2.8	The Arcade Learning Environment: An Evaluation Platform for General Agents	-
Gorila	-0.7	Massively Parallel Methods for Deep Reinforcement Learning	-
A3C LSTM hs	-6.4	Asynchronous Methods for Deep Reinforcement Learning	-
Prior hs	-5.3	Prioritized Experience Replay	-
MuZero	0.00	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model	-
ES FF (1 hour) noop	-4.5	Evolution Strategies as a Scalable Alternative to Reinforcement Learning	-
QR-DQN-1	23.6	Distributional Reinforcement Learning with Quantile Regression	-
DQN hs	11.1	Deep Reinforcement Learning with Double Q-learning	-

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