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 511–520 of 15113 papers

TitleStatusHype
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive Active InferenceCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
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Benchmark Results

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