71 lines
1.9 KiB
Markdown
71 lines
1.9 KiB
Markdown
# InteractionImitation
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Imitation Learning with the INTERACTION Dataset
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## Getting started
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Clone InteractionSimulator and pip install the module.
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```
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git clone https://github.com/sisl/InteractionSimulator.git
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cd InteractionSimulator
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pip install -e .
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cd ..
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export PYTHONPATH=$(pwd):$PYTHONPATH
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```
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Install additional requirements
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```
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pip install -r requirements.txt
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```
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Copy INTERACTION Dataset files:
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The INTERACTION dataset contains a two folders which should be copied into a folder called `./InteractionSimulator/datasets`:
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- the contents of `recorded_trackfiles` should be copied to `./InteractionSimulator/datasets/trackfiles`
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- the contents of `maps` should be copied to `./InteractionSimulator/datasets/maps`
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## Processing, saving, and loading expert demos
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Once the repository has been set up, you can process and save expert track demonstrations with:
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```
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python src/expert_data.py --loc [LOCNUM] --track [TRACKNUM]
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```
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You can then load the experts actions and observations using
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```
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from src import expert_data
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observations, actions = expert_data.load_expert_data(loc = [LOCNUM], track = [TRACKNUM])
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for (s, a) in zip (observations, actions):
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# do some imitation learning
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```
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## Package Structure
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```
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InteractionImitation
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|- demos
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|- algorithms
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|- BC
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|- AdVIL
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|- nets
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|- Encoder
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|- DeepSet
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|- Decoder
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|- policies
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|- discriminators
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|- demo_generators
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```
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## Type Definitions
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```
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Demo: List[Trajectory]
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Trajectory: List[Tuple[Observation, Action]] # single expert
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Observation: Dict[
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'own_state': [x, y, v, psi, psidot],
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'relative_states': List[[xr, yr, vr, psir, psidotr]],
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'own_path': List[[xr, yr]], # fixed length, constant dt
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'map': Map, # relative
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]
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Action: Range[0, 1]
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Policy: Union[
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Callable[[Observation], Action],
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Callable[[Observation, Action], probability],
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]
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Discriminator: Callable[[Observation, Action], value]
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Map: Dictionary[...]
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```
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