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 2621–2630 of 15113 papers

TitleStatusHype
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles—0
Autonomous Drone Racing with Deep Reinforcement Learning—0
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation—0
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model—0
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization—0
Show:102550
← PrevPage 263 of 1512Next →

Benchmark Results

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