Code collections
- https://github.com/sherjilozair/dqn a very basic DQN, which uses OpenAI's gym environment and Keras/Theano neural networks.
- https://github.com/tambetm/simple_dqn Deep Q-learning agent for replicating DeepMind's results in paper "Human-level control through deep reinforcement learning". Pure python
- https://github.com/osh/kerlym a handful of reinforcement learning agents implemented using the Keras
- https://github.com/rllab/rllab rllab is a framework for developing and evaluating reinforcement learning algorithms. It includes a wide range of continuous control tasks plus implementations. Theano based
- https://github.com/devsisters/DQN-tensorflow
Awesome collections
Paper collections
- https://github.com/junhyukoh/deep-reinforcement-learning-papers
- https://github.com/yiiwood/deep-reinforcement-learning-papers-1
- https://github.com/songrotek/deep-reinforcement-learning-papers-1
A quick tutorial
Alice->Bob: Hello Bob, how are you?
Note right of Bob: Bob thinks
Bob-->Alice: I am good thanks!
And flow charts like this:
st=>start: Start
e=>end
op=>operation: My Operation
cond=>condition: Yes or No?
st->op->cond
cond(yes)->e
cond(no)->op