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 9176–9200 of 15113 papers

TitleStatusHype
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number—0
Reinforcement Learning for Quantitative Trading—0
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks—0
Reinforcement Learning for Resilient Power Grids—0
Reinforcement Learning for Resource Allocation in Steerable Laser-based Optical Wireless Systems—0
Reinforcement Learning for Ridesharing: An Extended Survey—0
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction—0
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots—0
Reinforcement Learning for Robust Header Compression under Model Uncertainty—0
Reinforcement Learning for Robust Missile Autopilot Design—0
Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions—0
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions—0
Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks—0
Reinforcement Learning for Semantic Segmentation in Indoor Scenes—0
Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
Reinforcement Learning for Sociohydrology—0
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing—0
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment—0
Reinforcement Learning for Standards Design—0
Reinforcement Learning for Stock Transactions—0
Reinforcement Learning for Strategic Recommendations—0
Reinforcement learning for suppression of collective activity in oscillatory ensembles—0
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Benchmark Results

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