Reinforcement Learning
Tutorial Slides by Andrew Moore
You need to be happy about Markov Decision Processes (the previous Andrew Tutorial) before venturing into Reinforcement Learning.
Reinforcement Learning
Learning with a Critic
In supervised learning we have assumed that there is a target output value for each input value. However, in many situations, there is less detailed information available.
What is Reinforcement Learning?
Reinforcement learning is one of the most active research areas in Artificial Intelligence. Reinforcement learning is training by rewards and punishments. Here we train a computer as if we train a dog.
Introduction to Reinforcement Learning
In conjunction with the launch of the Knowledge Warehouse, I've just put up an overview of Reinforcement Learning. It gives a definition of the technique, covers motivation and applications.
Two varieities of reinforcement learning: Striatal & Prefrontal/Parietal?
[ Artificial Intelligence, Cognitive Neuroscience, Computational Modeling ]
Posted on: July 22, 2010 6:08 PM, by Chris Chatham ...
Reinforcement Learning
Like genetic algorithms, Reinforcement Learning is an unsupervised learning problem. However, unlike genetic algorithms, agents can learn during their lifetimes; it's not necessary to wait to see if they "live" or "die".
Reinforcement learning Learning how to act optimally in a given environment, especially with delayed and nondeterministic rewards. It is equivalent to adaptive control.
Reinforcement Learning
Learning through Reward & Punishment during Problem Solving
AITopics > Machine Learning > Reinforcement Learning ...
Reinforcement learning - The program interacts with its environment, with no set of known problems and solutions given, it can only learn from the results of its actions, which are rewards and punishments it receives from the environment.
Reinforcement Learning
Reinforcement learning (RL) is learning by interacting with an environment.
21 Reinforcement Learning
21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830
21.2 Passive Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 832
21.3 Active Reinforcement Learning . . . . . . .
[edit] Reinforcement learning
Main article: Reinforcement learning
Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward.
Reinforcement Learning With Self-Modifying Policies
Bayesian Q Learning by R. Dearden, N. Friedman, and S. Russell
Multigrid Q-Learning by Charles W. Anderson and Stewart G. Crawford-Hines ...
to machines to improve their performance. Such learning is usually referred to as 'machine learning'. Machine learning can be broadly classified into three categories: i) Supervised learning, ii) Unsupervised learning and iii) Reinforcement learning.
(The device U is not itself a reinforcement learning device; it is more like a 'Pavlovian' conditioning device, treating the Z signals as 'unconditioned' stimuli and the S signals as moves and replies.
reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world.
learning to learn --- where the algorithm learns its own inductive bias based on previous experience.
 See also: Neural network, Artificial intelligence, Knowledge, AI, Genetic algorithm
  
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