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 7276–7300 of 15113 papers

TitleStatusHype
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
Fast Algorithms for L_-constrained S-rectangular Robust MDPs—0
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation—0
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows—0
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning—0
Faster Deep Q-learning using Neural Episodic Control—0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Faster Reinforcement Learning with Expert State Sequences—0
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Benchmark Results

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