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 1326–1350 of 15113 papers

TitleStatusHype
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Maximum Mutation Reinforcement Learning for Scalable ControlCode1
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICsCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Continuous control with deep reinforcement learningCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Contrastive Active InferenceCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics MixtureCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
A Crash Course on 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
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Evolving Reinforcement Learning AlgorithmsCode1
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Benchmark Results

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