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

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Showing 4251–4260 of 15113 papers

TitleStatusHype
Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers—0
Policy Optimization over General State and Action Spaces—0
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords LearningCode0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Targets in Reinforcement Learning to solve Stackelberg Security Games—0
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Efficient Reinforcement Learning Through Trajectory GenerationCode1
General policy mapping: online continual reinforcement learning inspired on the insect brainCode0
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Benchmark Results

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