machine-learning   19517

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[1709.06009] Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.
machine-learning  benchmarking  games  to-write-about  nudge-targets  consider:performance-measures 
yesterday by Vaguery
[1611.00862] Quantile Reinforcement Learning
In reinforcement learning, the standard criterion to evaluate policies in a state is the expectation of (discounted) sum of rewards. However, this criterion may not always be suitable, we consider an alternative criterion based on the notion of quantiles. In the case of episodic reinforcement learning problems, we propose an algorithm based on stochastic approximation with two timescales. We evaluate our proposition on a simple model of the TV show, Who wants to be a millionaire.
machine-learning  performance-measure  representation  what-gets-measured-gets-fudged  to-write-about  nudge-targets 
yesterday by Vaguery
HazyResearch/snorkel
Training data creation and management system focused on information extraction http://snorkel.stanford.edu
machine-learning  information-extraction  training-data 
yesterday by mjlassila
Weak Supervision
Discusses the problems of creating sufficient amount of training data for DL.
machine-learning  theory  data-programming  snorkel  training 
yesterday by mjlassila
A Beginner’s Guide to AI/ML 🤖👶 – Machine Learning for Humans – Medium
The ultimate guide to machine learning. Simple, plain-English explanations accompanied by math, code, and real-world examples.
machinelearning  ai  machine-learning 
yesterday by andrewmarsh

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