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 6551–6575 of 15113 papers

TitleStatusHype
Deep Bellman Hedging—0
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach—0
Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates—0
Safe Reinforcement Learning for a Robot Being Pursued but with Objectives Covering More Than Capture-avoidance—0
q-Learning in Continuous Time—0
SURF: Semantic-level Unsupervised Reward Function for Machine Translation—0
Reinforcement Learning Based User-Guided Motion Planning for Human-Robot Collaboration—0
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning—0
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings—0
Partner Personas Generation for Dialogue Response Generation—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Interactive Query-Assisted Summarization via Deep Reinforcement LearningCode0
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony—0
Lifelong Inverse Reinforcement LearningCode0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
Learning Natural Language Generation with Truncated Reinforcement LearningCode0
Deep Reinforcement Learning with Swin TransformersCode0
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning—0
Performative Reinforcement Learning—0
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization—0
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference—0
Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement LearningCode0
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Benchmark Results

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