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 10751–10775 of 15113 papers

TitleStatusHype
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
Face valuing: Training user interfaces with facial expressions and reinforcement learning—0
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework—0
Factored Action Spaces in Deep Reinforcement Learning—0
Factored Adaptation for Non-Stationary Reinforcement Learning—0
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning—0
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning—0
Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel—0
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Fairness Incentives in Response to Unfair Dynamic Pricing—0
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Fairness in Reinforcement Learning—0
Fairness in Reinforcement Learning—0
Fairness in Reinforcement Learning: A Survey—0
Fake News Mitigation via Point Process Based Intervention—0
Falsification-Based Robust Adversarial Reinforcement Learning—0
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning—0
Fast active learning for pure exploration in reinforcement learning—0
Fast Adaptation with Behavioral Foundation Models—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
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Benchmark Results

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