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@@ -393,6 +393,22 @@ The agent has to pick up a box which is placed in a corner of a 3x3 maze.
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The doors are locked, the keys are hidden in boxes and doors are obstructed
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by balls. This environment can be solved without relying on language.
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+## Distributional shift environment
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+
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+Registered configurations:
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+- `MiniGrid-DistShift1-v0`
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+- `MiniGrid-DistShift2-v0`
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+
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+This environment is based on one of the DeepMind [AI safety gridworlds](https://github.com/deepmind/ai-safety-gridworlds).
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+The agent starts in the top-left corner and must reach the goal which is in the top-right corner, but has to avoid stepping
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+into lava on its way. The aim of this environment is to test an agent's ability to generalize. There are two slightly
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+different variants of the environment, so that the agent can be trained on one variant and tested on the other.
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+
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+<p align="center">
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+ <img src="figures/DistShift1.png" width="200">
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+ <img src="figures/DistShift2.png" width="200">
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+</p>
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+
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## Lava gap environment
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Registered configurations:
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@@ -431,22 +447,6 @@ has a single crossing point which can be safely used; Luckily, a path to the
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goal is guaranteed to exist. This environment is useful for studying safety and
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safe exploration.
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-## Distributional shift environment
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-
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-Registered configurations:
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-- `MiniGrid-DistShift1-v0`
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-- `MiniGrid-DistShift2-v0`
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-
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-This environment is based on one of the DeepMind [AI safety gridworlds](https://github.com/deepmind/ai-safety-gridworlds).
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-The agent starts in the top-left corner and must reach the goal which is in the top-right corner, but has to avoid stepping
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-into lava on its way. The aim of this environment is to test an agent's ability to generalize. There are two slightly
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-different variants of the environment, so that the agent can be trained on one variant and tested on the other.
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-
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-<p align="center">
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- <img src="figures/DistShift1.png" width="200">
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- <img src="figures/DistShift2.png" width="200">
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-</p>
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-
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## Simple crossing environment
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Registered configurations:
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Maxime Chevalier-Boisvert