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 9376–9400 of 15113 papers

TitleStatusHype
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Evolutionary Planning in Latent SpaceCode1
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing—0
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size SoccerCode0
Generative Adversarial Simulator—0
Distributed Deep Reinforcement Learning: An Overview—0
Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems—0
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks—0
On the Convergence of Reinforcement Learning in Nonlinear Continuous State Space Problems—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning ResearchCode1
MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty—0
Deep reinforcement learning for feedback control in a collective flashing ratchetCode0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
Online Model Selection for Reinforcement Learning with Function Approximation—0
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning—0
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space—0
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative FinanceCode3
Inverse Constrained Reinforcement LearningCode1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Inverse Reinforcement Learning via Matching of Optimality Profiles—0
Experimental Study on Reinforcement Learning-based Control of an Acrobot—0
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Benchmark Results

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