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 4181–4190 of 15113 papers

TitleStatusHype
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning—0
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation—0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
Belief-Enriched Pessimistic Q-Learning against Adversarial State PerturbationsCode0
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL—0
Language Guided Exploration for RL Agents in Text Environments—0
Koopman-Assisted Reinforcement Learning—0
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning—0
Twisting Lids Off with Two Hands—0
Show:102550
← PrevPage 419 of 1512Next →

Benchmark Results

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