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https://github.com/khushhallchandra/pytorch-rl

Pytorch Implementation of RL algorithms
https://github.com/khushhallchandra/pytorch-rl

artificial-intelligence ddpg deep-learning dqn openai-gym python pytorch reinforcement-learning reinforcement-learning-algorithms rl-agents

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Pytorch Implementation of RL algorithms

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# pytorch-rl
## DDPG

| | Un-Trained | Trained |
|----|--------------|------|
|DDPG|![pendulum untrained](/assets/pendulum_untrained.gif) |![pendulum trained](/assets/pendulum_trained.gif)
|DQN|![cartpole untrained](/assets/cartpole_untrained.gif) | ![cartpole trained](/assets/cartpole_trained.gif)
|CEM|![cartpole untrained](/assets/cem_untrained.gif) | ![cartpole trained](/assets/cem_trained.gif)

pytorch-rl is a python package based on pytorch library which aims to build standard deep reinforcement learning agent for the user to build various algorithms around it. pytorch-rl also includes some of the state-of-the-art implementations of popular deep reinforcement learning.
## Installation
pytorch-rl can be easily installed using `pip` as follows:
```
pip install pytorch-rl
```
This code currently uses python 2.7 and is compatible with both CPU and GPU (cuda 8.0).
## Usage
All the algorithmic implementations of a RL agent lies in the `rl` repository. While the RL agent (say DDPG) can be imported as follows,
```
from rl.agents import DDPGAgent
```
The standard DNN model associated with particular agent can be imported as follows
```
from rl.models.ddpg import ActorNetwork, CriticNetwork
```
Similarly, to run one of our RL agents( say DDPG) on a standard environment, simply run the following command
```
python examples/ddpg_pendulum.py
```
```
python examples/ddpg_cartpole.py
```
Agents implemeted currently
- [x] DDPG
- [x] DDQN
- [x] Duel DQN
- [x] CEM
- [x] PPO(needs to be tested)
- [x] ACKTR(needs to be tested)
- [x] A2C(needs to be tested)