<commit_before>Online Learning Agent
===========================
This package implements the maze-navigating agent from chapter 4 of Artificial Intelligence: A Modern Approach. The challenge is this: given a maze of known and unknown obstacles, create an agent that can continuously learn and navigate the environment.

## Quickstart
Running the agent simulation requires Python 3, pip, and optionally (but recommended) virtualenv.
```bash
$ git clone https://github.com/cpackard/learningagent.git && cd learningagent
$ virtualenv -p `which python3` learningagent && source learningagent/bin/activate
$ pip install -r requirements.txt
$ python -m learningagent.simulation
```

## Example
Below is an example simulation of the agent in an environment. The agent is penalized 1 point for every unit of distance moved, rewarded 1000 points every time it reaches the goal, and has 250 turns to maximize its score.

[![maze-agent](http://i.imgur.com/VlmpVvR.png)](https://youtu.be/XS-P-DK7nnE "maze-agent")

## Features
The agent boasts a number of features that allow it to excel in an unfamiliar territory, including:

- **Location Discovery:** The agent only has a map of the environment and the obstacles it can see around itself. Using these, the agent decides its possible locations and formulates the best plan to the goal.<commit_msg>Update project overview<commit_after>Maze Learning Agent
===========================
This package implements a maze-navigating agent capable of navigating towards a goal point in an unknown environment. Given only a partial map of the environment, the agent must:
- deduce its current location given the obstacles it can see,
- update its internal map of the environment as it explores, and
- attempt to reach the goal in the shortest distant possible

## Quickstart
Running the agent simulation requires Python 3, pip, and optionally (but recommended) virtualenv.
```bash
$ git clone https://github.com/cpackard/learningagent.git && cd learningagent
$ virtualenv -p `which python3` learningagent && source learningagent/bin/activate
$ pip install -r requirements.txt
$ python -m learningagent.simulation
```

## Example
Below is an example simulation of the agent in an environment. The agent is penalized 1 point for every unit of distance moved, rewarded 1000 points every time it reaches the goal, and has 250 turns to maximize its score.

[![maze-agent](http://i.imgur.com/VlmpVvR.png)](https://youtu.be/XS-P-DK7nnE "maze-agent")

## Features
The agent boasts a number of features that allow it to excel in an unfamiliar territory, including:

- **Location Discovery:** The agent only has a map of the environment and the obstacles it can see around itself. Using these, the agent decides its possible locations and formulates the best plan to the goal.
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