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 5251–5275 of 15113 papers

TitleStatusHype
Planning with RL and episodic-memory behavioral priors—0
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement LearningCode1
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial StatesCode0
Goal-Conditioned Generators of Deep PoliciesCode1
Safe Reinforcement Learning via Confidence-Based Filters—0
Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse SkillsCode0
Deep Bellman Hedging—0
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach—0
Stabilizing Off-Policy Deep Reinforcement Learning from PixelsCode1
USHER: Unbiased Sampling for Hindsight Experience ReplayCode0
Safe Reinforcement Learning for a Robot Being Pursued but with Objectives Covering More Than Capture-avoidance—0
Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates—0
q-Learning in Continuous Time—0
SURF: Semantic-level Unsupervised Reward Function for Machine Translation—0
Partner Personas Generation for Dialogue Response Generation—0
Learning Natural Language Generation with Truncated Reinforcement LearningCode0
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Interactive Query-Assisted Summarization via Deep Reinforcement LearningCode0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic—0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
Lifelong Inverse Reinforcement LearningCode0
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings—0
Reinforcement Learning Based User-Guided Motion Planning for Human-Robot Collaboration—0
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Benchmark Results

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