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 10376–10400 of 15113 papers

TitleStatusHype
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize—0
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
On Reward-Free Reinforcement Learning with Linear Function Approximation—0
NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration—0
A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines—0
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet—0
WD3: Taming the Estimation Bias in Deep Reinforcement Learning—0
Provably adaptive reinforcement learning in metric spaces—0
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs—0
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning—0
Deep Reinforcement Learning amidst Lifelong Non-Stationarity—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
DREAM: Deep Regret minimization with Advantage baselines and Model-free learningCode1
Learning Invariant Representations for Reinforcement Learning without ReconstructionCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and ControlCode0
Introduction to Machine Learning for Accelerator Physics—0
Learning to Track Dynamic Targets in Partially Known EnvironmentsCode1
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles—0
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Benchmark Results

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