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 8001–8050 of 15113 papers

TitleStatusHype
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
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning—0
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning—0
Optimising Turn-Taking Strategies With Reinforcement Learning—0
Acceleration in Policy Optimization—0
Optimism in Reinforcement Learning with Generalized Linear Function Approximation—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making—0
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization—0
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL—0
Optimistic PAC Reinforcement Learning: the Instance-Dependent View—0
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs—0
Optimistic Policy Optimization with Bandit Feedback—0
Optimistic Policy Optimization with General Function Approximations—0
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds—0
Optimistic Proximal Policy Optimization—0
Optimistic Reinforcement Learning by Forward Kullback-Leibler Divergence Optimization—0
Optimization Algorithm for Feedback and Feedforward Policies towards Robot Control Robust to Sensing Failures—0
Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications—0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Optimization of anemia treatment in hemodialysis patients via reinforcement learning—0
Optimization of Image Transmission in a Cooperative Semantic Communication Networks—0
Optimization of Multi-Factor Model in Quantitative Trading Based On Reinforcement Learning—0
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning—0
Optimization on a Budget: A Reinforcement Learning Approach—0
Optimization Solution Functions as Deterministic Policies for Offline Reinforcement Learning—0
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning—0
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning—0
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning—0
Optimizing AD Pruning of Sponsored Search with Reinforcement Learning—0
Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach—0
Optimizing Airborne Wind Energy with Reinforcement Learning—0
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective—0
Optimizing Automatic Differentiation with Deep Reinforcement Learning—0
Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF—0
Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning—0
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning—0
Optimizing Crop Management with Reinforcement Learning and Imitation Learning—0
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning—0
Optimizing Data Collection in Deep Reinforcement Learning—0
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach—0
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks—0
Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning—0
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction—0
Optimizing human-interpretable dialog management policy using Genetic Algorithm—0
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Benchmark Results

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