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 2481–2490 of 15113 papers

TitleStatusHype
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises—0
Continuous Control with Coarse-to-fine Reinforcement Learning—0
AWD3: Dynamic Reduction of the Estimation Bias—0
A model of discrete choice based on reinforcement learning under short-term memory—0
Avoiding Wireheading with Value Reinforcement Learning—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems—0
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Benchmark Results

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