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 3971–3980 of 15113 papers

TitleStatusHype
A Survey of Meta-Reinforcement Learning—0
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient—0
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNavCode1
Human-Timescale Adaptation in an Open-Ended Task Space—0
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehiclesCode1
DQNAS: Neural Architecture Search using Reinforcement Learning—0
Learning to solve arithmetic problems with a virtual abacus—0
Heterogeneous Multi-Robot Reinforcement LearningCode2
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness—0
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Benchmark Results

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