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项目作者： akaAlbo

项目描述 ：
Solution to the Deep RL Bootcamp labs from UC Berkeley
高级语言： Jupyter Notebook
项目地址: git://github.com/akaAlbo/deeprlbootcamp.git
创建时间： 2018-06-27T09:29:15Z
项目社区：https://github.com/akaAlbo/deeprlbootcamp
开源协议：
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Solutions to the Deep RL Bootcamp labs

  • Prelab: Set up your computer for all labs.
  • Lab 1: Markov Decision Processes. You will implement value iteration, policy iteration, and tabular Q-learning and apply these algorithms to simple environments including tabular maze navigation (FrozenLake) and controlling a simple crawler robot.
  • Lab 2: Introduction to Chainer. You will implement deep supervised learning using Chainer, and apply it to the MNIST dataset.
  • Lab 3: Deep Q-Learning. You will implement the DQN algorithm and apply it to Atari games.
  • Lab 4: Policy Optimization Algorithms. You will implement various policy optimization algorithms, including policy gradient, natural policy gradient, trust-region policy optimization (TRPO), and asynchronous advantage actor-critic (A3C). You will apply these algorithms to classic control tasks, Atari games, and roboschool locomotion environments.