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2 changes: 1 addition & 1 deletion docs/Getting-Started-with-Balance-Ball.md
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Expand Up @@ -269,7 +269,7 @@ on the same graph.

To summarize, go to your command line, enter the `ml-agents` directory and type:

```python
```
python3 python/learn.py <env_file_path> --run-id=<run-identifier> --train
```
**Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
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10 changes: 5 additions & 5 deletions docs/Learning-Environment-Examples.md
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Expand Up @@ -154,15 +154,15 @@ If you would like to contribute environments, please see our
![Banana](images/banana.png)

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

## Hallway
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