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 1601–1625 of 15113 papers

TitleStatusHype
MELD: Meta-Reinforcement Learning from Images via Latent State ModelsCode1
Memory-Augmented Reinforcement Learning for Image-Goal NavigationCode1
Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationCode1
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Meta-Reinforcement Learning of Structured Exploration StrategiesCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
METRA: Scalable Unsupervised RL with Metric-Aware AbstractionCode1
Mildly Conservative Q-Learning for Offline Reinforcement LearningCode1
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
Mirror Learning: A Unifying Framework of Policy OptimisationCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
Mitigating Open-Vocabulary Caption HallucinationsCode1
A Versatile and Efficient Reinforcement Learning Framework for Autonomous DrivingCode1
Model-Based Active ExplorationCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
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Benchmark Results

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