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Atari Games On Atari 2600 Boxing

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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Paper Title
MuZero (Res2 Adam)100Online and Offline Reinforcement Learning by Planning with a Learned Model
GDI-H3100GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
UCT100The Arcade Learning Environment: An Evaluation Platform for General Agents
Agent57100Agent57: Outperforming the Atari Human Benchmark
GDI-H3100Generalized Data Distribution Iteration
Ape-X100Distributed Prioritized Experience Replay
NoisyNet-Dueling100Noisy Networks for Exploration
MuZero100.00Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
GDI-I3100Generalized Data Distribution Iteration
IMPALA (deep)99.96IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
DNA99.9DNA: Proximal Policy Optimization with a Dual Network Architecture
QR-DQN-199.9Distributional Reinforcement Learning with Quantile Regression
IQN99.8Implicit Quantile Networks for Distributional Reinforcement Learning
ASL DDQN99.6Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
A2C + SIL99.6Self-Imitation Learning
Reactor 500M99.4The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Duel noop99.4Dueling Network Architectures for Deep Reinforcement Learning
DDQN+Pop-Art noop99.3Learning values across many orders of magnitude
Prior+Duel noop98.9Dueling Network Architectures for Deep Reinforcement Learning
R2D298.5Recurrent Experience Replay in Distributed Reinforcement Learning
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Atari Games On Atari 2600 Boxing | SOTA | HyperAI