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 5976–6000 of 15113 papers

TitleStatusHype
Self-Imitation Learning from Demonstrations—0
Long Short-Term Memory for Spatial Encoding in Multi-Agent Path PlanningCode0
Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes—0
Perceiving the World: Question-guided Reinforcement Learning for Text-based GamesCode0
Entailment Relation Aware Paraphrase Generation—0
Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions—0
Explicit User Manipulation in Reinforcement Learning Based Recommender Systems—0
Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study—0
MicroRacer: a didactic environment for Deep Reinforcement LearningCode0
Reinforcement learning reward function in unmanned aerial vehicle control tasks—0
Policy Gradients using Variational Quantum Circuits—0
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approachCode1
Teachable Reinforcement Learning via Advice DistillationCode1
Thompson Sampling on Asymmetric α-Stable Bandits—0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty—0
Privacy-Preserving Reinforcement Learning Beyond Expectation—0
Deep reinforcement learning guided graph neural networks for brain network analysis—0
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning—0
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning—0
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach—0
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination—0
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social NetworksCode0
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents—0
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Benchmark Results

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