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 4581–4590 of 15113 papers

TitleStatusHype
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals—0
Entropy Regularized Reinforcement Learning with Cascading Networks—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning—0
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning—0
Revisiting the Roles of "Text" in Text Games—0
A Multilevel Reinforcement Learning Framework for PDE-based ControlCode0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
DyFEn: Agent-Based Fee Setting in Payment Channel Networks—0
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems—0
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Benchmark Results

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