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 2501–2510 of 15113 papers

TitleStatusHype
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
Avoidance Learning Using Observational Reinforcement Learning—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
A Model-Based Reinforcement Learning Approach for PID Design—0
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance—0
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos—0
Average-Reward Reinforcement Learning with Entropy Regularization—0
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Benchmark Results

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