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 901–925 of 15113 papers

TitleStatusHype
Continuous control with deep reinforcement learningCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Discovering Reinforcement Learning AlgorithmsCode1
Discriminator-Weighted Offline Imitation Learning from Suboptimal DemonstrationsCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Goal-Conditioned Generators of Deep PoliciesCode1
Goal-Conditioned Reinforcement Learning: Problems and SolutionsCode1
Contrastive Active InferenceCode1
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement LearningCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Gradient Imitation Reinforcement Learning for Low Resource Relation ExtractionCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic EnvironmentsCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
Accelerating Exploration with Unlabeled Prior DataCode1
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Benchmark Results

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