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 826–850 of 15113 papers

TitleStatusHype
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Differentiable Trust Region Layers for Deep Reinforcement LearningCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement LearningCode1
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
Active Inference for Stochastic ControlCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning PoliciesCode1
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
Co-designing Intelligent Control of Building HVACs and MicrogridsCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Exploration via Planning for Information about the Optimal TrajectoryCode1
Adaptive Contention Window Design using Deep Q-learningCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement LearningCode1
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Learning to Manipulate Deformable Objects without DemonstrationsCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
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Benchmark Results

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