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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1676–1700 of 15113 papers

TitleStatusHype
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human EnvironmentsCode1
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
BabyAI 1.1Code1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Neural Inventory Control in Networks via Hindsight Differentiable Policy OptimizationCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
NICE: Robust Scheduling through Reinforcement Learning-Guided Integer ProgrammingCode1
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential EquationsCode1
Neural Ordinary Differential Equation Control of Dynamics on GraphsCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Object Detection with Deep Reinforcement LearningCode1
Objective Mismatch in Model-based Reinforcement LearningCode1
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning PoliciesCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Offline Meta-Reinforcement Learning with Advantage WeightingCode1
Offline Meta-Reinforcement Learning with Online Self-SupervisionCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Co-designing Intelligent Control of Building HVACs and MicrogridsCode1
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Benchmark Results

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