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 3821–3830 of 15113 papers

TitleStatusHype
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
Near-Optimal Adversarial Reinforcement Learning with Switching Costs—0
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints—0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Predictable MDP Abstraction for Unsupervised Model-Based RLCode1
Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement LearningCode0
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Show:102550
← PrevPage 383 of 1512Next →

Benchmark Results

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