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

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Showing 7751–7775 of 15113 papers

TitleStatusHype
Weighted model estimation for offline model-based reinforcement learning—0
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization—0
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration—0
Model-Based Reinforcement Learning via Imagination with Derived Memory—0
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation—0
Reinforcement Learning in Newcomblike Environments—0
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat—0
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculumCode0
Counterexample Guided RL Policy Refinement Using Bayesian OptimizationCode0
Curriculum Offline Imitating Learning—0
Improving gearshift controllers for electric vehicles with reinforcement learning—0
Design and Development of Spoken Dialogue System in Indic Languages—0
Distributionally Robust Imitation Learning—0
Explicable Reward Design for Reinforcement Learning AgentsCode0
Fast Algorithms for L_-constrained S-rectangular Robust MDPs—0
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning—0
Continuous Control With Ensemble Deep Deterministic Policy GradientsCode0
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—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
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware ImpairmentsCode0
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