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 3441–3450 of 15113 papers

TitleStatusHype
Deep Learning of Koopman Representation for Control—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
DeepMDP: Learning Continuous Latent Space Models for Representation Learning—0
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning—0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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