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 461–470 of 15113 papers

TitleStatusHype
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
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Benchmark Results

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