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 3401–3425 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
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs—0
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
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
Towards Generalizable Reinforcement Learning for Trade Execution—0
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient AlgorithmsCode0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
Optimizing Memory Mapping Using Deep Reinforcement Learning—0
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas—0
Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning—0
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes—0
Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning AlgorithmCode1
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
RLocator: Reinforcement Learning for Bug Localization—0
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement LearningCode1
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
Policy Gradient Methods in the Presence of Symmetries and State AbstractionsCode1
Information Design in Multi-Agent Reinforcement LearningCode1
Knowledge-enhanced Agents for Interactive Text Games—0
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Benchmark Results

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