awjuliani/DeepRL-Agents
A collection of deep reinforcement learning agents (DQN, policy gradient, Q-learning variants) implemented in Tensorflow, designed as accompanying tutorial material.
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This repository provides Tensorflow implementations of core reinforcement learning algorithms including Q-learning variants, policy gradient methods, and deep Q-networks. The implementations are structured as Jupyter notebooks designed to accompany a Medium tutorial series on reinforcement learning, covering classic environments like CartPole and contextual bandit problems.
Frequently asked
- What is awjuliani/DeepRL-Agents?
- A collection of deep reinforcement learning agents (DQN, policy gradient, Q-learning variants) implemented in Tensorflow, designed as accompanying tutorial material.
- Is DeepRL-Agents open source?
- Yes — awjuliani/DeepRL-Agents is open source, released under the MIT license.
- What language is DeepRL-Agents written in?
- awjuliani/DeepRL-Agents is primarily written in Jupyter Notebook.
- How popular is DeepRL-Agents?
- awjuliani/DeepRL-Agents has 2.3k stars on GitHub.
- Where can I find DeepRL-Agents?
- awjuliani/DeepRL-Agents is on GitHub at https://github.com/awjuliani/DeepRL-Agents.