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 2261–2270 of 15113 papers

TitleStatusHype
Bridging State and History Representations: Understanding Self-Predictive RLCode1
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement LearningCode1
Behaviour Discovery and Attribution for Explainable Reinforcement Learning—0
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
Behavioural Cloning in VizDoom—0
Behavior Regularized Offline Reinforcement Learning—0
Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning—0
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning—0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
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Benchmark Results

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