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 4041–4050 of 15113 papers

TitleStatusHype
A Comparison of learning algorithms on the Arcade Learning Environment—0
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
A Comparison of Self-Play Algorithms Under a Generalized Framework—0
A Complementary Learning Systems Approach to Temporal Difference Learning—0
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management—0
A Computational Framework for Motor Skill Acquisition—0
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
A Concise Introduction to Reinforcement Learning in Robotics—0
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Benchmark Results

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