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 10276–10300 of 15113 papers

TitleStatusHype
Learning Intrinsic Symbolic Rewards in Reinforcement Learning—0
Maximum Reward Formulation In Reinforcement LearningCode0
Nonstationary Reinforcement Learning with Linear Function Approximation—0
Provable Fictitious Play for General Mean-Field Games—0
Regularized Inverse Reinforcement Learning—0
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving—0
Online Safety Assurance for Deep Reinforcement Learning—0
Variational Intrinsic Control Revisited—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Actor-Critic Algorithm for High-dimensional Partial Differential Equations—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited—0
Learning Diverse Options via InfoMax Termination CriticCode0
Heterogeneous Multi-Agent Reinforcement Learning for Unknown Environment Mapping—0
Safety Aware Reinforcement Learning (SARL)—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Sentiment Analysis for Reinforcement Learning—0
Meta-Learning of Structured Task Distributions in Humans and MachinesCode0
Policy Learning Using Weak SupervisionCode0
The act of remembering: a study in partially observable reinforcement learning—0
Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows—0
Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks—0
Learning to Generalize for Sequential Decision MakingCode0
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning—0
Goal-directed Generation of Discrete Structures with Conditional Generative Models—0
Show:102550
← PrevPage 412 of 605Next →

Benchmark Results

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