tetrisrl 0.2

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## Installation
```bash
$ git clone https://github.com/jaybutera/tetrisRL
```

## Layout
* dqn_agent.py - DQN reinforcement learning agent trains on tetris
* supervised_agent.py - The same convolutional model as DQN trains on a dataset of user playthroughs
* user_engine.py - Play tetris and accumulate information as a training set
* run_model.py - Evaluate a saved agent model on a visual game of tetris (i.e.)
```bash
$ python run_model.py checkpoint.pth.tar
```

## Usage

### Using the Environment
The interface is similar to an [OpenAI Gym](https://gym.openai.com/docs) environment.

Initialize the Tetris RL environment

```python
from engine import TetrisEngine

width, height = 10, 20
env = TetrisEngine(width, height)
```

Simulation loop
```python
# Reset the environment
obs = env.clear()

while True:
# Get an action from a theoretical AI agent
action = agent(obs)

# Sim step takes action and returns results
obs, reward, done = env.step(action)

# Done when game is lost
if done:
break
```

### Play Tetris for Training Data
Play games and accumulate a data set for a supervised learning algorithm to
trian on. An element of data stores a
(state, reward, done, action) tuple for each frame of the game.

You may notice the rules are slightly different than normal Tetris.
Specifically, each action you take will result in a corresponding soft drop
This is how the AI will play and therefore how the training data must be taken.

To play Tetris:
```bash
$ python user_engine.py
```

Controls:
W: Hard drop (piece falls to the bottom)
A: Shift left
S: Soft drop (piece falls one tile)
D: Shift right
Q: Rotate left
E: Rotate right

At the end of each game, choose whether you want to store the information of
that game in the data set.