Gridworld

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Simple implementation of text-based Gridworld game. Intended for use with reinforcement learning algorithms.

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# Gridworld
Simple implementation of text-based Gridworld game. Intended for use with reinforcement learning algorithms.

As described in this blog post: < http://outlace.com/rlpart3.html >

### Basic usage:

```python
from Gridworld import *
```

Initialize game state using `initGrid` (initializes stationary grid, all items are placed deterministically), `initGridPlayer` (initializes player in random location, but keep wall, goal and pit stationary), or `initGridRand` in which all pieces are initialized to random locations on the board.

```python
game = Gridworld(size=4, mode='static')
game.dispGrid()
```

```
array([['+', '-', ' ', 'P'],
       [' ', 'W', ' ', ' '],
       [' ', ' ', ' ', ' '],
       [' ', ' ', ' ', ' ']], dtype='<U2')

```

"P" represents the player, "W" is the wall (an obstacle), and "-" is the pit (a trap that gives a negative reward) and "+" is the goal which gives positive reward and wins the game.

The player makes moves with the `game.makeMove(move)` where  `move` is a character `u,d,l, or r` for up,down, left or right.

```python
game.makeMove('d') # (0,3) + (-1,3)
print("Reward: %s" % (game.getReward()))
game.dispGrid()
```

```
Reward: -1

array([['+', '-', ' ', ' '],
       [' ', 'W', ' ', 'P'],
       [' ', ' ', ' ', ' '],
       [' ', ' ', ' ', ' ']], dtype='<U2')
```

Use the `render_np()` method to produce a numpy tensor to supply to a machine learning algorithm.
```python
game.board.render_np()
```

```
array([[[0, 0, 0, 0],
        [0, 0, 0, 1],
        [0, 0, 0, 0],
        [0, 0, 0, 0]],

[[1, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]],

[[0, 1, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]],

[[0, 0, 0, 0],
        [0, 1, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]]], dtype=uint8)

```

Gridworld

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