# Tag Info

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In the context of reinforcement learning, the idea of modeling your goal-oriented problem as a hierarchy of multiple sub-problems is called hierarchical reinforcement learning, which gives rise to concepts such as semi-Markov decision processes and options (aka macro actions). The article The Promise of Hierarchical Reinforcement Learning presents and ...

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The video you linked is not using reinforcement learning (RL). It is using genetic algorithms (GA). GA is designed around using multiple agents and picking the best performing to move forward to next generation. With this approach, it is common to want to only view the best performing agents, as the learning mechanism uses the same selection process - the ...

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Model your problem as an MDP To solve a problem with reinforcement learning, you need to model your problem as a Markov decision process (MDP), so you need to define the state space, the action space, and the reward function of the MDP. Understand your problem and the goal To do define these, you need to understand your problem and define it as a goal-...

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One 'easy' way would be to have some sort of conversational memory, where you track what the user has said already. I don't know how complex your patterns are, but if you could recognise names and track references, you could try and build up a mental model of the user's relationships with other people, and perhaps refer to that in your bots responses. The ...

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Probably to as many as possible. Average accept rate of papers is around 20%. You can find the best conferences on AI & ML Event.

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Well, it depends on the structure of the data. The best way is to try all the intelligent models like naive bayes, random forest, svm with different parameters by grid search. There is no model works best all the time for classification. However, neural network (named Multilayer Perceptron on weka) is supposed to be better if it is set correctly.

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The tendency in literature in the last years (at least for computer vision problems) seems to point towards the single model option (I'll try to remember to come back and add some links to papers mentioning this when I find them), although this IMO is really data- and problem-dependent. In your case, I would set up a network for the mapping $x$ to $g(x)$, ...

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