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 3481–3490 of 15113 papers

TitleStatusHype
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning—0
Towards Controllable Diffusion Models via Reward-Guided Exploration—0
Car-Following Models: A Multidisciplinary Review—0
Exploring the Noise Resilience of Successor Features and Predecessor Features Algorithms in One and Two-Dimensional Environments—0
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation—0
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resamplingCode0
Show:102550
← PrevPage 349 of 1512Next →

Benchmark Results

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