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 4951–4975 of 15113 papers

TitleStatusHype
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm—0
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
Reinforcement Learning for Visual Object Detection—0
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy—0
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos—0
Reinforcement Learning Framework for Opportunistic Routing in WSNs—0
Reinforcement Learning Framework for Quantitative Trading—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints—0
Reinforcement Learning from Bagged Reward—0
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel—0
Reinforcement Learning from Diverse Human Preferences—0
Reinforcement Learning from Imperfect Demonstrations—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—0
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Reinforcement Learning-Guided Semi-Supervised Learning—0
Reinforcement Learning in 20Q Game with Generic Knowledge Bases—0
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space—0
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
Reinforcement Learning in a large scale photonic Recurrent Neural Network—0
Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP—0
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization—0
Reinforcement Learning in Categorical Cybernetics—0
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Benchmark Results

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