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 9876–9900 of 15113 papers

TitleStatusHype
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Development of collective behavior in newborn artificial agents—0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance—0
Dexterous Manipulation through Imitation Learning: A Survey—0
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost—0
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management—0
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Dialog Action-Aware Transformer for Dialog Policy Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Dialogue Shaping: Empowering Agents through NPC Interaction—0
DiBB: Distributing Black-Box Optimization—0
Dichotomy of Control: Separating What You Can Control from What You Cannot—0
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning—0
Difference of Convex Functions Programming Applied to Control with Expert Data—0
Difference of Convex Functions Programming for Reinforcement Learning—0
Difference Rewards Policy Gradients—0
Differentiable Arbitrating in Zero-sum Markov Games—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
Differentiable Logic Machines—0
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
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Benchmark Results

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