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 10476–10500 of 15113 papers

TitleStatusHype
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach—0
Mobile Networks for Computer Go—0
DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents—0
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication—0
Adversarial Imitation Learning via Random Search—0
Model-Free Episodic Control with State Aggregation—0
NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks—0
Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems—0
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting—0
Model-free optimal control of discrete-time systems with additive and multiplicative noises—0
Static Neural Compiler Optimization via Deep Reinforcement Learning—0
Intelligent Replication Management for HDFS Using Reinforcement Learning—0
A Survey of Knowledge-based Sequential Decision Making under Uncertainty—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Adaptive trading strategies across liquidity pools—0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer—0
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation—0
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation—0
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningCode0
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning—0
Reinforcement Learning for Improving Object Detection—0
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization—0
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Benchmark Results

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