Reinforcement Learning and Decision Making - Algorithms, Markov Decision Processes (MDPs), Temporal Difference (TD) Lambda, Convergence, Analysis, Exploration, Exploitation, Generalisation, Game Theory, Coordinating, Communicating, Coaching (CCC)

✍ Scribed by Various

Tongue: English
Leaves: 533
Category: Library

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✦ Table of Contents

Motivation: Hierarchical Reinforcement Learning
Experimental Results
Learning with Options
Options and Random Exploration
Other Applications of Options
Summary
Introduction
Reinforcement Learning
Related Work
Policy Shaping
Model Parameters
Estimating a Policy from Feedback
Reconciling Policy Information from Multiple Sources
Experimental Setup
Pac-Man
Frogger
Constructing an Oracle
Experiments
A Comparison to the State of the Art
How The Reward Parameter Affects Action Biasing
How Domain Size Affects Learning
Using an Inaccurate Estimate of Feedback Consistency
Discussion
Conclusion

✦ Subjects

artificial intelligence; machine learning; AI; ML; DL; SL; UL; deep learning; reinforcement learning; RL; supervised learning; unsupervised learning; optimization; optimisation; advanced algorithmic analysis; AAA; deep; DRL; deep RL; information theory; cybernetics; data analysis; statistics; inference; statistical; probability; statistics; MDPs; markovian; control theory; robotics; multi-agent; agent; economics; conflict; linear algebra; advanced topics; partially observable; POMDPs; CS 7642; C

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