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 3401–3410 of 15113 papers

TitleStatusHype
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning ResearchCode3
Deep Reinforcement Learning-based Exploration of Web ApplicationsCode0
What Matters in Reinforcement Learning for TractographyCode1
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality—0
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension—0
Task-Oriented Communication Design at Scale—0
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs—0
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes—0
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback—0
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular NetworksCode0
Show:102550
← PrevPage 341 of 1512Next →

Benchmark Results

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