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 4091–4100 of 15113 papers

TitleStatusHype
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error—0
Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators—0
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning—0
Understanding the Complexity Gains of Single-Task RL with a Curriculum—0
SHIRO: Soft Hierarchical Reinforcement Learning—0
Automated Gadget Discovery in ScienceCode0
Deep Reinforcement Learning for Heat Pump Control—0
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information—0
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Benchmark Results

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