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 2181–2190 of 15113 papers

TitleStatusHype
Data-Incremental Continual Offline Reinforcement Learning—0
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning AgentsCode0
Actor-Critic Reinforcement Learning with Phased Actor—0
Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
LTL-Constrained Policy Optimization with Cycle Experience Replay—0
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem—0
Automated Discovery of Functional Actual Causes in Complex Environments—0
Sustainability of Data Center Digital Twins with Reinforcement LearningCode2
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Benchmark Results

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