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

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Showing 10176–10200 of 15113 papers

TitleStatusHype
Dual Behavior Regularized Reinforcement Learning—0
Dual Control for Approximate Bayesian Reinforcement Learning—0
Dual Ensemble Kalman Filter for Stochastic Optimal Control—0
Dual Generator Offline Reinforcement Learning—0
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations—0
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study—0
Dueling RL: Reinforcement Learning with Trajectory Preferences—0
DyFEn: Agent-Based Fee Setting in Payment Channel Networks—0
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery—0
Dynamically meeting performance objectives for multiple services on a service mesh—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping—0
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning—0
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
Dynamic-Depth Context Tree Weighting—0
Dynamic Dialogue Policy for Continual Reinforcement Learning—0
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning—0
Dynamic Experience Replay—0
Dynamic Face Video Segmentation via Reinforcement Learning—0
Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories—0
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning—0
Dynamic Input for Deep Reinforcement Learning in Autonomous Driving—0
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Benchmark Results

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