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 4401–4425 of 15113 papers

TitleStatusHype
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
A Survey of Reinforcement Learning from Human Feedback—0
Optimizing Heat Alert Issuance with Reinforcement LearningCode0
Maximum entropy GFlowNets with soft Q-learning—0
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
Parameterized Projected Bellman OperatorCode0
Optimal coordination of resources: A solution from reinforcement learning—0
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning—0
Data-Driven Merton's Strategies via Policy Randomization—0
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement LearningCode0
Active search and coverage using point-cloud reinforcement learning—0
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum LearningCode0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
Assume-Guarantee Reinforcement Learning—0
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
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Benchmark Results

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