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Commit eb2ee82e authored by Erik Nygren's avatar Erik Nygren :bullettrain_front:
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Merge branch 'patch-1' into 'master'

Fix typos

See merge request !10
parents 4b9c3107 06690f76
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1 merge request!10Fix typos
......@@ -60,7 +60,7 @@ For training purposes the tree is flattend into a single array.
## Training
### Setting up the environment
Before you get started with the training make sure that you have [pytorch](https://pytorch.org/get-started/locally/) installed.
Let us now train a simle double dueling DQN agent to navigate to its target on flatland. We start by importing flatland
Let us now train a simPle double dueling DQN agent to navigate to its target on flatland. We start by importing flatland
```
from flatland.envs.generators import complex_rail_generator
......@@ -111,7 +111,7 @@ env_renderer = RenderTool(env, gl="PILSVG", )
To set up a appropriate agent we need the state and action space sizes. From the discussion above about the tree observation we end up with:
[**Adrian**: I just wonder, why this is not done in seperate method in the the observation: get_state_size, then we don't have to write down much more. And the user don't need to
understand anything about the oberservation. I suggest moving this into the obersvation, base ObservationBuilder declare it as an abstract method. ... ]
understand anything about the observation. I suggest moving this into the observation, base ObservationBuilder declare it as an abstract method. ... ]
```
# Given the depth of the tree observation and the number of features per node we get the following state_size
......@@ -218,7 +218,7 @@ for trials in range(1, n_trials + 1):
eps = max(eps_end, eps_decay * eps) # decrease epsilon
```
Running the `navigation_training.py` file trains a simple agent to navigate to any random target within the railway network. After running you should see a learning curve similiar to this one:
Running the `training_navigation.py` file trains a simple agent to navigate to any random target within the railway network. After running you should see a learning curve similiar to this one:
![Learning_curve](https://i.imgur.com/yVGXpUy.png)
......
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