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 1326–1350 of 15113 papers

TitleStatusHype
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
Entropy-Regularized Process Reward ModelCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence GenerationCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Evaluating Soccer Player: from Live Camera to Deep Reinforcement LearningCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic CandidatesCode1
Combining Modular Skills in Multitask LearningCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
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Benchmark Results

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