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 10926–10950 of 15113 papers

TitleStatusHype
Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning—0
Intelligent Residential Energy Management System using Deep Reinforcement Learning—0
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement LearningCode0
Time-Variant Variational Transfer for Value Functions—0
Towards intervention-centric causal reasoning in learning agents—0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
A reinforcement learning approach to rare trajectory samplingCode0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
Active Measure Reinforcement Learning for Observation Cost Minimization—0
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model—0
Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games—0
Gradient Monitored Reinforcement Learning—0
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce—0
Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Deep Reinforcement Learning Based Power Allocation for D2D Network—0
Deep Learning Models for Automatic Summarization—0
Policy Entropy for Out-of-Distribution Classification—0
Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications—0
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks—0
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic—0
Model-free Reinforcement Learning for Stochastic Stackelberg Security Games—0
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning—0
Automatic Discovery of Interpretable Planning StrategiesCode0
Evaluating Generalisation in General Video Game Playing—0
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Benchmark Results

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