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Marwan Mattar
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Merge pull request #575 from Unity-Technologies/hotfix-docs-banana-collector
Hotfix docs banana collector
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docs/Getting-Started-with-Balance-Ball.md

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To summarize, go to your command line, enter the `ml-agents` directory and type:
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```python
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```
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python3 python/learn.py <env_file_path> --run-id=<run-identifier> --train
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```
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**Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.

docs/Learning-Environment-Examples.md

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![Banana](images/banana.png)
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* Set-up: A multi-agent environment where agents compete to collect bananas.
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* Goal: The agents must learn to move to as many yellow bananas as possible while avoiding red bananas.
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* Agents: The environment contains 10 agents linked to a single brain.
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* Goal: The agents must learn to move to as many yellow bananas as possible while avoiding blue bananas.
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* Agents: The environment contains 5 agents linked to a single brain.
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* Agent Reward Function (independent):
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* +1 for interaction with yellow banana
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* -1 for interaction with red banana.
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* -1 for interaction with blue banana.
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* Brains: One brain with the following observation/action space.
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* Vector Observation space: (Continuous) 51 corresponding to velocity of agent, plus ray-based perception of objects around agent's forward direction.
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* Vector Observation space: (Continuous) 53 corresponding to velocity of agent (2), whether agent is frozen and/or shot its laser (2), plus ray-based perception of objects around agent's forward direction (49; 7 raycast angles with 7 measurements for each).
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* Vector Action space: (Continuous) Size of 3, corresponding to forward movement, y-axis rotation, and whether to use laser to disable other agents.
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* Visual Observations (Optional): First-person view for each agent.
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* Visual Observations (Optional; None by default): First-person view for each agent.
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* Reset Parameters: None
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## Hallway

unity-environment/Assets/ML-Agents/Examples/BananaCollectors/BananaRL.unity

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