arXiv:2103.04910
공식 코드 · Papers with Code
A Crash Course on Reinforcement Learning
원문 초록
The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches to solve an RL problem: Policy Gradient, Policy Iteration, and Model-building. Dynamical systems might have discrete action-space like cartpole where two possible actions are +1 and -1 or continuous action space like linear Gaussian systems. Our discussion covers both cases.
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High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Exploring the robustness of TractOracle methods in RL-based tractography
Deep Reinforcement Learning with Gradient Eligibility Traces
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
토론
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