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 2521–2530 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites—0
Unknown mixing times in apprenticeship and reinforcement learning—0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
Average-reward model-free reinforcement learning: a systematic review and literature mapping—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Average-Reward Learning and Planning with Options—0
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Benchmark Results

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