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 2731–2740 of 15113 papers

TitleStatusHype
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement LearningCode0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and ConstraintsCode0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Digital Twin-Native AI-Driven Service Architecture for Industrial Networks—0
Evaluating Pretrained models for Deployable Lifelong Learning—0
Risk-sensitive Markov Decision Process and Learning under General Utility Functions—0
Large Language Model as a Policy Teacher for Training Reinforcement Learning AgentsCode1
Learning to Fly in SecondsCode2
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?Code0
Show:102550
← PrevPage 274 of 1512Next →

Benchmark Results

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