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 3541–3550 of 15113 papers

TitleStatusHype
Optimal Transport for Offline Imitation LearningCode1
Learning to Operate in Open Worlds by Adapting Planning Models—0
Communication Load Balancing via Efficient Inverse Reinforcement Learning—0
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction—0
Deep RL with Hierarchical Action Exploration for Dialogue Generation—0
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models—0
Beam Management Driven by Radio Environment Maps in O-RAN Architecture—0
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale—0
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Benchmark Results

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