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@@ -4,7 +4,15 @@ There are other gridworld Gym environments out there, but this one is
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designed to be particularly simple, lightweight and fast. The code has very few
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dependencies, making it less likely to break or fail to install. It loads no
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external sprites/textures, and it can run at up to 5800 FPS on a quad-core
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-laptop, which means you can run your experiments faster.
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+laptop, which means you can run your experiments faster. Batteries are
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+included: a known-working RL implementation is supplied in this repository
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+to help you get started.
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+
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+Requirements:
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+- Python 3
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+- OpenAI gym
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+- numpy
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+- PyQT for graphics
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This environment has been built at the [MILA](https://mila.quebec/en/) as
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part of the [Baby AI Game](https://github.com/maximecb/baby-ai-game) project.
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@@ -71,11 +79,11 @@ You can view the result of training using the `enjoy.py` script:
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python3 basicrl/enjoy.py --env-name MiniGrid-Empty-6x6-v0 --load-dir ./trained_models/acktr
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```
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-## Features
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+## Design
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The environment is partially observable and uses a compact and efficient
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encoding, with just 3 inputs per visible grid cell. It is also easy to
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-produce pixels for observations if desired.
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+produce an array of pixels for observations if desired.
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Each cell/tile in the grid world contains one object, each object has an
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associated discrete color. The objects currently supported are walls, doors,
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@@ -85,7 +93,6 @@ forward and pickup/toggle to interact with objects. The agent can carry
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one carryable item at a time (eg: ball or key). By default, only sparse
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rewards for reaching the goal square are provided.
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-Design choices were made to try to keep everything as simple as possible.
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Extending the environment with new object types and dynamics should be
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very easy. If you wish to do this, you should take a look at
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the [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py) source file.
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@@ -93,6 +100,9 @@ the [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py) source file.
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## Included Environments
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The environments listed below are implemented in the [gym_minigrid/envs](/gym_minigrid/envs) directory.
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+Each environment provides one or more configurations registered with OpenAI gym. Each environment
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+is also programmatically tunable in terms of size/complexity, which is useful for curriculum learning
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+or to fine-tune difficulty.
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### Empty environment
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Maxime Chevalier-Boisvert