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 8601–8625 of 15113 papers

TitleStatusHype
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning—0
Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning—0
Optimal Management of the Peak Power Penalty for Smart Grids Using MPC-based Reinforcement Learning—0
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning—0
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies—0
Optimal Neuron Selection: NK Echo State Networks for Reinforcement Learning—0
Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks—0
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling—0
Optimal Operating Strategy for PV-BESS Households: Balancing Self-Consumption and Self-Sufficiency—0
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints—0
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem—0
Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning—0
Optimal Portfolio Liquidation—0
Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning—0
Optimal Reinforcement Learning for Gaussian Systems—0
Optimal Sample Complexity of Reinforcement Learning for Mixing Discounted Markov Decision Processes—0
Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning—0
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach—0
Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting—0
Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach—0
Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning—0
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints—0
Optimal Use of Experience in First Person Shooter Environments—0
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach—0
Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage—0
Show:102550
← PrevPage 345 of 605Next →

Benchmark Results

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