Implementation of the Categorical DQN introduced in A distributional Perspective on Reinforcement Learning.
Alongside is an implementation of the standard DQN.
Code is not optimized...yet.
- Python3.5
Install the game of Catcher:
git clone https://github.com/ioanachelu/gym_fast_envs
cd gym_fast_envs
pip install -r requirements.txt
pip install -e .
- You can run basic DQN or CategoricalDQN. General flags can be found in
configs/base_flags.py
. You can edit this file or use-algorithm="DQN"
or-algorithm="CategoricalDQN"
- For specific algorithm parameter consult
configs/categorical_dqn_flags.py
andconfigs/dqn_flags.py
-
To train use:
python run.py python run.py --resume=True
-
To eval use:
python run.py --resume=False
-
To see training progress run tensorboard from the
summaries/CategoricalDQN
orsummaries/DQN
directory:tenorboard --logdir=.
- Add learning rate schedule
- Add evaluation procedure
- Better switch between agents
- Add result plots
- Atari