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 8001–8025 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
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Benchmark Results

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