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

Showing 24512475 of 15113 papers

TitleStatusHype
Learning Bellman Complete Representations for Offline Policy EvaluationCode0
Learning Action-Transferable Policy with Action EmbeddingCode0
Learning Actionable Representations with Goal-Conditioned PoliciesCode0
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEsCode0
Learning Curriculum Policies for Reinforcement LearningCode0
AutoRL Hyperparameter LandscapesCode0
Autoregressive Policies for Continuous Control Deep Reinforcement LearningCode0
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the CloudCode0
Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and SearchCode0
A Meta-MDP Approach to Exploration for Lifelong Reinforcement LearningCode0
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement LearningCode0
Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement LearningCode0
Latent Intention Dialogue ModelsCode0
Latent Guided Sampling for Combinatorial OptimizationCode0
Adaptive Ordered Information Extraction with Deep Reinforcement LearningCode0
LatentPoison - Adversarial Attacks On The Latent SpaceCode0
Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement LearningCode0
Large Language Models are Autonomous Cyber DefendersCode0
Large Language Model-Driven Curriculum Design for Mobile NetworksCode0
Large Language Models are Biased Reinforcement LearnersCode0
Learning data augmentation policies using augmented random searchCode0
Autonomous robotic nanofabrication with reinforcement learningCode0
Language Model Alignment with Elastic ResetCode0
Language as an Abstraction for Hierarchical Deep Reinforcement LearningCode0
Langevin DQNCode0
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Benchmark Results

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