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 4231–4240 of 15113 papers

TitleStatusHype
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning—0
Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning—0
CT-DQN: Control-Tutored Deep Reinforcement Learning—0
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Launchpad: Learning to Schedule Using Offline and Online RL Methods—0
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task EnvironmentsCode1
Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System—0
Modeling Mobile Health Users as Reinforcement Learning Agents—0
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Benchmark Results

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