SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 2531–2540 of 15113 papers

TitleStatusHype
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot—0
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement LearningCode1
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
Solving Offline Reinforcement Learning with Decision Tree RegressionCode0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
Large-scale Reinforcement Learning for Diffusion Models—0
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough ReproductionCode0
Crowd-PrefRL: Preference-Based Reward Learning from Crowds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified