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 3801–3810 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
A Model-Based Reinforcement Learning Approach for PID Design—0
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Correlation Priors for Reinforcement Learning—0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
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Benchmark Results

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