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 4131–4140 of 15113 papers

TitleStatusHype
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal NavigationCode0
Latent Variable Representation for Reinforcement Learning—0
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
Reinforcement Learning for Agile Active Target Sensing with a UAV—0
Offline Reinforcement Learning for Visual NavigationCode1
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Reinforcement Learning in Credit Scoring and Underwriting—0
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Benchmark Results

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