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 4331–4340 of 15113 papers

TitleStatusHype
Data-Driven Offline Decision-Making via Invariant Representation Learning—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Real-time Local Feature with Global Visual Information Enhancement—0
Safe Reinforcement Learning using Data-Driven Predictive Control—0
Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling—0
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal ControlCode0
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing FlowsCode1
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop SchedulingCode1
Adversarial Cheap TalkCode3
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies—0
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Benchmark Results

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