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 10976–11000 of 15113 papers

TitleStatusHype
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Fragment-based Sequential Translation for Molecular Optimization—0
FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Free^2Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models—0
Free Energy Projective Simulation (FEPS): Active inference with interpretability—0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic—0
Free Will Belief as a consequence of Model-based Reinforcement Learning—0
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback—0
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility—0
FROM DEEP LEARNING TO DEEP DEDUCING: AUTOMATICALLY TRACKING DOWN NASH EQUILIBRIUM THROUGH AUTONOMOUS NEURAL AGENT, A POSSIBLE MISSING STEP TOWARD GENERAL A.I.—0
From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation—0
From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning—0
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation—0
From Imitation to Refinement -- Residual RL for Precise Assembly—0
From internal models toward metacognitive AI—0
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following—0
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards—0
From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning—0
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning—0
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Benchmark Results

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