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SOTA
Atari Games
Atari Games On Atari 2600 Ice Hockey
Atari Games On Atari 2600 Ice Hockey
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Score
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Score
Paper Title
Repository
Prior+Duel hs
0.5
Deep Reinforcement Learning with Double Q-learning
IMPALA (deep)
3.48
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
DNA
7.2
DNA: Proximal Policy Optimization with a Dual Network Architecture
Nature DQN
-1.6
Human level control through deep reinforcement learning
NoisyNet-Dueling
3
Noisy Networks for Exploration
DQN hs
-1.6
Deep Reinforcement Learning with Double Q-learning
MuZero
67.04
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
CGP
4
Evolving simple programs for playing Atari games
IQN
0.2
Implicit Quantile Networks for Distributional Reinforcement Learning
GDI-I3
44.94
Generalized Data Distribution Iteration
-
C51 noop
-3.5
A Distributional Perspective on Reinforcement Learning
Best Learner
-9.5
The Arcade Learning Environment: An Evaluation Platform for General Agents
DreamerV2
26
Mastering Atari with Discrete World Models
Agent57
63.64
Agent57: Outperforming the Atari Human Benchmark
A2C + SIL
-2.4
Self-Imitation Learning
QR-DQN-1
-1.7
Distributional Reinforcement Learning with Quantile Regression
A3C LSTM hs
-1.7
Asynchronous Methods for Deep Reinforcement Learning
ASL DDQN
-3.6
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
-
Prior hs
-0.2
Prioritized Experience Replay
A3C FF (1 day) hs
-4.7
Asynchronous Methods for Deep Reinforcement Learning
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