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 5426–5450 of 15113 papers

TitleStatusHype
Federated Offline Reinforcement Learning—0
An application of neural networks to a problem in knot theory and group theory (untangling braids)—0
Large-Scale Retrieval for Reinforcement Learning—0
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement LearningCode0
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement LearningCode1
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC—0
Multifidelity Reinforcement Learning with Control Variates—0
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS—0
Regret Bounds for Information-Directed Reinforcement Learning—0
Mildly Conservative Q-Learning for Offline Reinforcement LearningCode1
Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement LearningCode1
Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems—0
Receding Horizon Inverse Reinforcement Learning—0
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes—0
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-RiskCode1
Overcoming the Spectral Bias of Neural Value Approximation—0
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information—0
Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning—0
Challenges and Opportunities in Offline Reinforcement Learning from Visual ObservationsCode2
Deep Surrogate Assisted Generation of Environments—0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
Reinforced Inverse Scattering—0
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Benchmark Results

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