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 11776–11800 of 15113 papers

TitleStatusHype
Value-Added Chemical Discovery Using Reinforcement Learning—0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy—0
Worst Cases Policy Gradients—0
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning—0
Learning to reinforcement learn for Neural Architecture SearchCode0
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning—0
Contrastive Multi-document Question GenerationCode0
Option Compatible Reward Inverse Reinforcement Learning—0
H_ Model-free Reinforcement Learning with Robust Stability GuaranteeCode0
Experience Sharing Between Cooperative Reinforcement Learning Agents—0
Distributional Reward Decomposition for Reinforcement Learning—0
Improving reinforcement learning algorithms: towards optimal learning rate policiesCode0
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports—0
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems—0
Quinoa: a Q-function You Infer Normalized Over Actions—0
Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices—0
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement LearningCode0
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning—0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms—0
Robotic Tracking Control with Kernel Trick-based Reinforcement Learning—0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
Online Robustness Training for Deep Reinforcement Learning—0
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning—0
Non-Cooperative Inverse Reinforcement Learning—0
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Benchmark Results

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