This project includes PyTorch implementations of Deep Q-Network algorithm for single agent.
- DQN
It is written in a modular way to allow for sharing code between different algorithms. In specific, each algorithm is represented as a learning agent with a unified interface including the following components:
- interact: interact with the environment to collect experience. Taking one step forward and n steps forward are both supported (see
_take_one_step_and_take_n_steps, respectively) - train: train on a sample batch
- exploration_action: choose an action based on state with random noise added for exploration in training
- action: choose an action based on state for execution
- value: evaluate value for a state-action pair
- evaluation: evaluation the learned agent
- python 3.6+
- gym
- pytorch
To train a model:
$ python run_dqn_mod.py
To inference a model:
$ python inference_dqn.py
It's extremely difficult to reproduce results for Reinforcement Learning algorithms. Due to different settings, e.g., random seed and hyper parameters etc, you might get different results compared with the followings.
This project gets inspirations from the following projects:
- Ilya Kostrikov's pytorch-a2c-ppo-acktr (kfac optimizer is taken from here)
- OpenAI's baselines
MIT