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 11576–11600 of 15113 papers

TitleStatusHype
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Dota 2 with Large Scale Deep Reinforcement LearningCode0
Lessons from reinforcement learning for biological representations of space—0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Provably Efficient Reinforcement Learning with Aggregated States—0
More Efficient Off-Policy Evaluation through Regularized Targeted Learning—0
The PlayStation Reinforcement Learning Environment (PSXLE)Code0
Provably Efficient Exploration in Policy Optimization—0
Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model—0
Learning to Reach Goals via Iterated Supervised LearningCode0
Improved Activity Forecasting for Generating Trajectories—0
Control-Tutored Reinforcement Learning—0
Biases for Emergent Communication in Multi-agent Reinforcement Learning—0
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning—0
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning—0
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning—0
Quality of syntactic implication of RL-based sentence summarization—0
Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments—0
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable EnvironmentsCode0
Measuring the Reliability of Reinforcement Learning AlgorithmsCode0
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos—0
Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
Learning to Code: Coded Caching via Deep Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
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Benchmark Results

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