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Reinforcement Learning: An Introduction

The text is now complete, except possibly for one more case study to be added to Chapter 16. The references still need to be thoroughly ...

incompleteideas.net·12.0 MB
Reinforcement Learning: A Tutorial

estimation, recoding data based on some principle, etc. 3. Reinforcement Learning. • learning approaches to sequential decision making. • learning from a critic ...

web.eecs.umich.edu·10.1 MB
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
Discovering Reinforcement Learning Algorithms

Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research.

papers.neurips.cc·2.5 MB
Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure ...

sites.ualberta.ca·1.5 MB
Action Elimination with Deep Reinforcement Learning

by T Zahavy · Cited by 298 — Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many ...

proceedings.neurips.cc·1.5 MB
Reinforcement Learning in Robotics: A Survey

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the.

publications.ri.cmu.edu·1.4 MB
Cooperative Inverse Reinforcement Learning

by D Hadfield-Menell · Cited by 1168 — More precisely, we define a cooperative inverse reinforcement learning (CIRL) game as a two-player game of partial information, in which the “human”, H, knows ...

people.eecs.berkeley.edu·515.7 KB

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