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Update Docs: HTTP -> HTTPS (#813)

URLs updated to use HTTPS protocol where appropriate.
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him2him2 authored and gdb committed Dec 25, 2017
1 parent 509c2c0 commit 0c91364cd4a7ea70f242a28b85c3aea2d74aa35a
Showing with 3 additions and 3 deletions.
  1. +1 −1 LICENSE.md
  2. +2 −2 docs/agents.md
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@@ -2,7 +2,7 @@
The MIT License
Copyright (c) 2016 OpenAI (http://openai.com)
Copyright (c) 2016 OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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@@ -22,7 +22,7 @@ This is a very basic DQN (with experience replay) implementation, which uses Ope
## Simple DQN
Simple, fast and easy to extend DQN implementation using [Neon](https://github.com/NervanaSystems/neon) deep learning library. Comes with out-of-box tools to train, test and visualize models. For details see [this blog post](http://www.nervanasys.com/deep-reinforcement-learning-with-neon/) or check out the [repo](https://github.com/tambetm/simple_dqn).
Simple, fast and easy to extend DQN implementation using [Neon](https://github.com/NervanaSystems/neon) deep learning library. Comes with out-of-box tools to train, test and visualize models. For details see [this blog post](https://www.nervanasys.com/deep-reinforcement-learning-with-neon/) or check out the [repo](https://github.com/tambetm/simple_dqn).
## AgentNet
A library that allows you to develop custom deep/convolutional/recurrent reinforcement learning agent with full integration with Theano/Lasagne. Also contains a toolkit for various reinforcement learning algorithms, policies, memory augmentations, etc.
@@ -36,4 +36,4 @@ a framework for developing and evaluating reinforcement learning algorithms, ful
## [keras-rl](https://github.com/matthiasplappert/keras-rl)
[keras-rl](https://github.com/matthiasplappert/keras-rl) implements some state-of-the art deep reinforcement learning algorithms. It was built with OpenAI Gym in mind, and also built on top of the deep learning library [Keras](http://keras.io/) and utilises similar design patterns like callbacks and user-definable metrics.
[keras-rl](https://github.com/matthiasplappert/keras-rl) implements some state-of-the art deep reinforcement learning algorithms. It was built with OpenAI Gym in mind, and also built on top of the deep learning library [Keras](https://keras.io/) and utilises similar design patterns like callbacks and user-definable metrics.

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