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 6701–6725 of 15113 papers

TitleStatusHype
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
Weighted model estimation for offline model-based reinforcement learning—0
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Offline Model-based Adaptable Policy LearningCode1
BooVI: Provably Efficient Bootstrapped Value Iteration—0
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculumCode0
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning—0
Curriculum Offline Imitating Learning—0
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Counterexample Guided RL Policy Refinement Using Bayesian OptimizationCode0
Distributionally Robust Imitation Learning—0
Explicable Reward Design for Reinforcement Learning AgentsCode0
EDGE: Explaining Deep Reinforcement Learning PoliciesCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning—0
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning—0
Model-Free μ Synthesis via Adversarial Reinforcement Learning—0
The Power of Communication in a Distributed Multi-Agent System—0
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—0
Continuous Control With Ensemble Deep Deterministic Policy GradientsCode0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning—0
Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network—0
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement LearningCode0
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Benchmark Results

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