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 3831–3840 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing—0
Deep Reinforcement Learning with Linear Quadratic Regulator Regions—0
Deep Reinforcement Learning With Macro-Actions—0
Assume-Guarantee Reinforcement Learning—0
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft—0
Deep Reinforcement Learning with Plasticity Injection—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
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Benchmark Results

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