Deep-RL-agents
This repository contains the work I did during my Reinforcement Learning internship from September 2017 to February 2018.
During these 6 months, I reimplemented the main deep-RL algorithms that have been developped since 2013, using only Tensorflow and numpy. This repository contains implementations of :
- A3C : the 2016 algorithm that uses asynchronous gradient descent for optimization on multi-CPU instead of a single GPU
- C51 : the 2017 algorithm that explores the idea of predicting not only the value of a state, but instead the value distribution
- DDPG : the 2015 algorithm that tackles the problem of continuous control using an actor-critic architecture
- Rainbow : the 2017 algorithm that combines six classical extensions to DQN
- D4PG : the 2018 algorithm that applies the distributional approach to a DDPG with an asynchronous architecture
The general architecture of these algorithm is always the same :
- the main.py file initialize the agent and run it
- the Model.py file implements the Neural Network (actor-critic or not, with convolution or not)
- the QNetwork.py file instantiates a Network and build the tensorflow operations to perform the gradient descent to train it
- the Agent.py file implements the agent class that interacts with the environment in order to get experiences
- the settings.py file is used to change the hyperparameters of the algorithm and the network
Others directories include :
- utils : a set of classes and functions used in other algorithms
- BlogFiles : a jupyter notebook that tries to explain the idea behind A3C, DDPG and Rainbow
- Environment Test : copies from the main algorithms set up to run in specific environments
- GIF : a set of GIF saved after having trained different agents on many environments