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 3561–3570 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Discovers Internal Models—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
A statistical learning strategy for closed-loop control of fluid flows—0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
Deep Reinforcement Learning for Adaptive Traffic Signal Control—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Show:102550
← PrevPage 357 of 1512Next →

Benchmark Results

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