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 9676–9700 of 15113 papers

TitleStatusHype
Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning—0
Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation—0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs—0
Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning—0
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning—0
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning ApproachCode0
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards—0
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning AgentsCode0
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design—0
Deep Learning of Koopman Representation for Control—0
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning AlgorithmsCode0
Optimal Dispatch in Emergency Service System via Reinforcement Learning—0
Constrained Model-based Reinforcement Learning with Robust Cross-Entropy MethodCode1
Masked Contrastive Representation Learning for Reinforcement LearningCode1
Multi-Agent Trust Region Policy OptimizationCode0
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous ControlCode1
Local Differential Privacy for Regret Minimization in Reinforcement Learning—0
Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic SatisfactionCode0
Self-Imitation Learning for Robot Tasks with Sparse and Delayed RewardsCode0
Modeling Protagonist Emotions for Emotion-Aware StorytellingCode1
UAV Path Planning using Global and Local Map Information with Deep Reinforcement LearningCode1
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Benchmark Results

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