mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-16 21:10:08 +00:00
c62e9259db
* Add support for custom objects * Add python 3.8 to the CI * Bump version * PyType fixes * [ci skip] Fix typo * Add note about slow-down + fix typos * Minor edits to the doc * Bug fix for DQN * Update test * Add test for custom objects
135 lines
2.7 KiB
ReStructuredText
135 lines
2.7 KiB
ReStructuredText
.. _dqn:
|
|
|
|
.. automodule:: stable_baselines3.dqn
|
|
|
|
|
|
DQN
|
|
===
|
|
|
|
`Deep Q Network (DQN) <https://arxiv.org/abs/1312.5602>`_ builds on `Fitted Q-Iteration (FQI) <http://ml.informatik.uni-freiburg.de/former/_media/publications/rieecml05.pdf>`_
|
|
and make use of different tricks to stabilize the learning with neural networks: it uses a replay buffer, a target network and gradient clipping.
|
|
|
|
|
|
.. rubric:: Available Policies
|
|
|
|
.. autosummary::
|
|
:nosignatures:
|
|
|
|
MlpPolicy
|
|
CnnPolicy
|
|
|
|
|
|
Notes
|
|
-----
|
|
|
|
- Original paper: https://arxiv.org/abs/1312.5602
|
|
- Further reference: https://www.nature.com/articles/nature14236
|
|
|
|
.. note::
|
|
This implementation provides only vanilla Deep Q-Learning and has no extensions such as Double-DQN, Dueling-DQN and Prioritized Experience Replay.
|
|
|
|
|
|
Can I use?
|
|
----------
|
|
|
|
- Recurrent policies: ❌
|
|
- Multi processing: ❌
|
|
- Gym spaces:
|
|
|
|
|
|
============= ====== ===========
|
|
Space Action Observation
|
|
============= ====== ===========
|
|
Discrete ✔ ✔
|
|
Box ❌ ✔
|
|
MultiDiscrete ❌ ✔
|
|
MultiBinary ❌ ✔
|
|
============= ====== ===========
|
|
|
|
|
|
Example
|
|
-------
|
|
|
|
.. code-block:: python
|
|
|
|
import gym
|
|
import numpy as np
|
|
|
|
from stable_baselines3 import DQN
|
|
|
|
env = gym.make("CartPole-v0")
|
|
|
|
model = DQN("MlpPolicy", env, verbose=1)
|
|
model.learn(total_timesteps=10000, log_interval=4)
|
|
model.save("dqn_pendulum")
|
|
|
|
del model # remove to demonstrate saving and loading
|
|
|
|
model = DQN.load("dqn_pendulum")
|
|
|
|
obs = env.reset()
|
|
while True:
|
|
action, _states = model.predict(obs, deterministic=True)
|
|
obs, reward, done, info = env.step(action)
|
|
env.render()
|
|
if done:
|
|
obs = env.reset()
|
|
|
|
|
|
Results
|
|
-------
|
|
|
|
Atari Games
|
|
^^^^^^^^^^^
|
|
|
|
The complete learning curves are available in the `associated PR #110 <https://github.com/DLR-RM/stable-baselines3/pull/110>`_.
|
|
|
|
|
|
How to replicate the results?
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
|
|
Clone the `rl-zoo repo <https://github.com/DLR-RM/rl-baselines3-zoo>`_:
|
|
|
|
.. code-block:: bash
|
|
|
|
git clone https://github.com/DLR-RM/rl-baselines3-zoo
|
|
cd rl-baselines3-zoo/
|
|
|
|
|
|
Run the benchmark (replace ``$ENV_ID`` by the env id, for instance ``BreakoutNoFrameskip-v4``):
|
|
|
|
.. code-block:: bash
|
|
|
|
python train.py --algo dqn --env $ENV_ID --eval-episodes 10 --eval-freq 10000
|
|
|
|
|
|
Plot the results:
|
|
|
|
.. code-block:: bash
|
|
|
|
python scripts/all_plots.py -a dqn -e Pong Breakout -f logs/ -o logs/dqn_results
|
|
python scripts/plot_from_file.py -i logs/dqn_results.pkl -latex -l DQN
|
|
|
|
|
|
Parameters
|
|
----------
|
|
|
|
.. autoclass:: DQN
|
|
:members:
|
|
:inherited-members:
|
|
|
|
.. _dqn_policies:
|
|
|
|
DQN Policies
|
|
-------------
|
|
|
|
.. autoclass:: MlpPolicy
|
|
:members:
|
|
:inherited-members:
|
|
|
|
.. autoclass:: stable_baselines3.dqn.policies.DQNPolicy
|
|
:members:
|
|
:noindex:
|
|
|
|
.. autoclass:: CnnPolicy
|
|
:members:
|