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Reinforcement Learning: An Introduction

We first came to focus on what is now known as reinforcement learning in late 1979. We were both at the University of Massachusetts, working on one of the ...

neuro.bstu.by·5.2 MB
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[PDF] Model-Based Reinforcement Learning

by S Levine · Cited by 6 — • Understand the terminology and formalism of model-based RL. • Understand the options for models we can use in model-based RL. • Understand practical ...

rail.eecs.berkeley.edu·3.7 MB
Deep Reinforcement Learning CS 294 - 112

Deep models are what allow reinforcement learning algorithms to solve complex problems end to end! Page 21. Complex physical tasks… Rajeswaran, et al. 2018 ...

rail.eecs.berkeley.edu·3.5 MB
Self-Paced Deep Reinforcement Learning

Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout ...

proceedings.nips.cc·787.6 KB
[PDF] Reinforcement Learning

2. to introduce students to the concepts based on Markov Decision Process, Dynamic. Programming, Monte Carlo methods, and Temporal-Difference learning.

cs.du.ac.in·778.2 KB
[PDF] CMPUT 628: Deep Reinforcement Learning

The course will take a concepts-based approach instead of focusing on particular algorithms, and it will also touch on empirical design and current reporting.

webdocs.cs.ualberta.ca·354.2 KB

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