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 2771–2780 of 15113 papers

TitleStatusHype
Accelerating Exploration with Unlabeled Prior DataCode1
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
LLM Augmented Hierarchical Agents—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Stable Modular Control via Contraction Theory for Reinforcement Learning—0
Low-Rank MDPs with Continuous Action Spaces—0
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy OptimizationCode1
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
High-dimensional Bid Learning for Energy Storage Bidding in Energy Markets—0
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments—0
Show:102550
← PrevPage 278 of 1512Next →

Benchmark Results

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