Metadata-Version: 2.1
Name: fastrlapi
Version: 0.0.1
Summary: reinforcement learning high-level API.
Home-page: UNKNOWN
Author: Rohit Gangupantulu
Author-email: rgangupantulu@deloitte.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Requires-Dist: tensorflow (==2.0.1)
Requires-Dist: tensorflow-probability (==0.8.0)
Requires-Dist: tf-agents (==0.3.0)
Requires-Dist: tensorforce (==0.5.3)
Requires-Dist: gym (==0.15.4)
Requires-Dist: imageio (==2.6.1)
Requires-Dist: imageio-ffmpeg (==0.3.0)
Requires-Dist: matplotlib (==3.1.2)

### Reinforcement Learning API made simple - FAST_RLAPI

fastrlapi is a high level reinforcement learning api focusing on ease of use and simplicity.
Written in Python and running on top of established reinforcement learning libraries like
[tf-Agents](https://github.com/tensorflow/agents), 
[tensorforce](https://github.com/tensorforce/tensorforce) or 
[keras-rl](https://github.com/keras-rl/keras-rl).


### Examples
---
````
from fastrlapi.agents import PpoAgent
from fastrlapi.callbacks import plot

ppoAgent = PpoAgent('CartPole-v0')
ppoAgent.train([plot.State(), plot.Loss(), plot.Rewards()])
````


#### More Detailed
````
from fastrlapi.agents import PpoAgent
from fastrlapi.callbacks import plot

ppoAgent = PpoAgent( 'Orso-v1',fc_layers=(500,500,500))
ppoAgent.train([plot.State(), plot.Loss(), plot.Rewards(), plot.Actions(), 
                plot.StepRewards(), plot.Steps(), plot.ToMovie()], 
                learning_rate = 0.0001, num_iterations = 500, max_steps_per_episode=50 )
````

### Available Algorithms and Backends
---

|algorithm | [tf-Agents](https://github.com/tensorflow/agents) | [tensorforce](https://github.com/tensorforce/tensorforce) | [keras-rl (suspended)](https://github.com/keras-rl/keras-rl) | fastrlapi class name |
|----------|:---------:|:-----------:|:--------:| :---: | 
|[CEM](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.81.6579&rep=rep1&type=pdf) |`not available`  |`not available`  |`yes`  | CemAgent | 
|[Dqn](https://arxiv.org/abs/1312.5602)           |`yes`            |`yes`    |`yes`            | DqnAgent | 
|[Double Dqn](https://arxiv.org/abs/1509.06461)   |`open`           |`not available`    |`yes`  | DoubleDqnAgent|
|[Dueling Dqn](https://arxiv.org/abs/1511.06581)  |`not available`  |`yes`    |`yes`            | DuelingDqnAgent|
|[Ppo](https://arxiv.org/abs/1707.06347)          |`yes`            |`yes`    |`not available`  | PpoAgent |
|Random                                           |`yes`            |`yes`    |`not available`  | RandomAgent |
|[REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)  |`yes`  |`yes` |`not available`| ReinforceAgent | 
|[SAC](https://arxiv.org/abs/1801.01290)          |`preview`        |`not available` |`not available`|SacAgent|


### Installation
---
Install from pypi using pip:

```python
pip install fastrlapi
```


