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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4651–4675 of 15113 papers

TitleStatusHype
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiencyCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement LearningCode0
Navigating Uncertainty in ESG Investing—0
Towards a Unified Framework for Sequential Decision Making—0
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning—0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression—0
Improving Dialogue Management: Quality Datasets vs ModelsCode0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Controlling Neural Style Transfer with Deep Reinforcement Learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning—0
Learning to Rewrite Prompts for Personalized Text Generation—0
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and SmoothnessCode0
Reinforcement Learning for Node Selection in Branch-and-Bound—0
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
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Benchmark Results

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