Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

June 20, 2017 · Entered Twilight · 🏛 AAAI Conference on Artificial Intelligence

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Repo contents: .gitignore, BayesianNeuralNetwork.py, ExperienceReplay.py, HiPMDP.py, LICENSE.txt, PriorityQueue.py, Qnetwork.py, README.md, acrobot_simulator, grid_simulator, hiv_simulator, preset_parameters, toy_example.ipynb

Authors Taylor Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez arXiv ID 1706.06544 Category stat.ML: Machine Learning (Stat) Cross-listed cs.AI, cs.LG Citations 110 Venue AAAI Conference on Artificial Intelligence Repository https://github.com/dtak/hip-mdp-public ⭐ 30 Last Checked 1 month ago

Abstract

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.