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

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Showing 5176–5200 of 15113 papers

TitleStatusHype
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
Smoothed Q-learning—0
Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems—0
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active MeasuringCode0
Fast Rates for Maximum Entropy ExplorationCode0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Kernel Density Bayesian Inverse Reinforcement LearningCode0
Deploying Offline Reinforcement Learning with Human Feedback—0
Loss of Plasticity in Continual Deep Reinforcement Learning—0
Actor-Critic learning for mean-field control in continuous time—0
Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks—0
Path Planning using Reinforcement Learning: A Policy Iteration Approach—0
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication—0
The tree reconstruction game: phylogenetic reconstruction using reinforcement learning—0
Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi—0
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs—0
Optimal foraging strategies can be learnedCode0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning—0
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective—0
Real-time scheduling of renewable power systems through planning-based reinforcement learning—0
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards—0
Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach—0
Task Aware Dreamer for Task Generalization in Reinforcement Learning—0
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning—0
Beware of Instantaneous Dependence in Reinforcement Learning—0
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Benchmark Results

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