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 1826–1850 of 15113 papers

TitleStatusHype
Reduced Policy Optimization for Continuous Control with Hard ConstraintsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Regulatory DNA sequence Design with Reinforcement LearningCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Reinforced Epidemic Control: Saving Both Lives and EconomyCode1
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and ReviewCode1
Reinforcement Learning and Tree Search Methods for the Unit Commitment ProblemCode1
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
Reinforcement Learning-Based Automatic Berthing SystemCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate ControlCode1
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
Reinforcement Learning-based Placement of Charging Stations in Urban Road NetworksCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic RewardsCode1
Reinforcement Learning for Ballbot Navigation in Uneven TerrainCode1
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective TrajectoriesCode1
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?Code1
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge DesignCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary MissionsCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic SystemsCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
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Benchmark Results

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