An area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

2019-05-15 · Q-Learning algorithm along with an implementation in Python using Numpy.

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2020-07-02 · Important reinforcement learning (RL) algorithms, including policy iteration, Q-Learning, and Neural Fitted Q.

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2020-07-01 · Plot replications, exercise solutions and Anki flashcards for the entire book by chapters.

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2018-02-19 · In this post, we are gonna briefly go over the field of Reinforcement Learning (RL), from fundamental concepts to classic algorithms.

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2020-01-29 · Curriculum learning applied to reinforcement learning, with a few exceptions of supervised learning.

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2020-06-07 · Exploitation versus exploration is a critical topic in reinforcement learning. This post introduces several common approaches for better exploration in ...

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2019-06-23 · Explore cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently.

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2020-01-29 · Curriculum learning applied to reinforcement learning, with a few exceptions of supervised learning.

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2020-06-01 · A library of reinforcement learning components and agents.

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2020-06-27 · "rlx" is a Deep RL library written on top of PyTorch & built for educational and research purpose.

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2020-05-15 · ESTorch is an Evolution Strategy Library build around PyTorch.

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A PyTorch library for building deep reinforcement learning agents.

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