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 5001–5025 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Plasticity Injection—0
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness—0
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Constrained Proximal Policy Optimization—0
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry—0
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and PracticeCode0
Offline Primal-Dual Reinforcement Learning for Linear MDPs—0
Lagrangian-based online safe reinforcement learning for state-constrained systems—0
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
BertRLFuzzer: A BERT and Reinforcement Learning Based FuzzerCode0
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning—0
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL—0
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
Client Selection for Federated Policy Optimization with Environment HeterogeneityCode0
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning PoliciesCode0
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning—0
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCSCode0
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions—0
Coagent Networks: Generalized and Scaled—0
Cooperation Is All You Need—0
Show:102550
← PrevPage 201 of 605Next →

Benchmark Results

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