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 10226–10250 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
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation—0
Uncertainty-aware Contact-safe Model-based Reinforcement Learning—0
Multi-Agent Trust Region Policy OptimizationCode0
Optimal Dispatch in Emergency Service System via Reinforcement Learning—0
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards—0
Local Differential Privacy for Regret Minimization in Reinforcement Learning—0
Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning—0
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning ApproachCode0
Deep Learning of Koopman Representation for Control—0
Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning—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
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning AlgorithmsCode0
Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic SatisfactionCode0
Self-Imitation Learning for Robot Tasks with Sparse and Delayed RewardsCode0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Deep Reinforcement Learning and Transportation Research: A Comprehensive Review—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search—0
Balancing Constraints and Rewards with Meta-Gradient D4PG—0
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Benchmark Results

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