semvec/pypi-packages huggingface dataset:
HighwayEnv-iPLAN 0.0.1
Codebase for for our version of highway-way modified for iPLAN paper

# Highway-env for iPLAN
Our forked version of [highway-env](https://github.com/Farama-Foundation/HighwayEnv) used in our paper:

[iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://arxiv.org/abs/2306.06236)

More details could be found in the main page of [highway-env](https://github.com/Farama-Foundation/HighwayEnv)
and [iPLAN](https://github.com/wuxiyang1996/iPLAN).

# Installation
```angular2html
pip install highway_env_iPLAN
```

# Major Changes
* Add two behavior-driven vehicle models, `DefensiveVehicle` and `AggressiveVehicle` in `vehicle/behavior.py`.
* Add multi-agent support for `Highway` scenario given in `envs/highway_env.py`, modify the `MultiAgentWrapper`
in `vehicle/common/abstract.py`.
* Add three heterogeneous traffic scenarios, `HighwayEnvHetero`, `HighwayEnvHetero_H` and `HighwayEnvHetero_VH`
in `envs/highway_env.py`, with vehicle ID broadcasting and different behavior-driven vehicles.
* Add multi-agent support for visualization in `Highway` scenario that allows a camera following each agent
and visualize their surroundings from their respective viewpoints.

# Animation
The animation shows 5 such learning agents (Green) with their surroundings from their respective viewpoints. 
Behavior-driven vehicles in the environment include: Normal (Blue), Aggressive (Purple) and Defensive (Yellow).
Vehicles terminate (Red) when colliding with other vehicles.

<p align="center">
    <img src="animation/iPLAN_Hetero_H_5_90.0_21.81.gif"><br/>
    <em> iPLAN in chaotic (hard) scenario of Heterogeneous Highway 
    (Num of agents succeed: 5, Avg. survival time: 90, Avg. speed: 21.81).</em>
</p>
bec-client-lib 0.6.14
BEC Client library
Requires Python: >=3.8

# BEC Client Lib

bec-client-lib is a Python library communicating with the [Beamline and Experiment Control (BEC)](https://gitlab.psi.ch/bec/bec) server. It is primarily used to build new BEC clients such as graphical user interfaces (GUIs) or command line interfaces (CLIs).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install bec-client-lib.

```bash
pip install bec-client-lib
```

## Documentation

The documentation is part of the BEC documentation and can be found [here](https://bec.readthedocs.io/en/latest/).

## Usage

```python
from bec_client_lib import BECClient

# Create a new BECClient instance and start it
bec = BECClient()
bec.start()

# Convenient access to scans and devices
scans = bec.scans
dev = bec.device_manager.devices

# define a dummy callback function
def dummy_callback(data, metadata):
    print(data, metadata)

# add the callback and subscribe to the scan segments
bec.callbacks.register(event_type="scan_segment", callback=dummy_callback, sync=False)

```

## Contributing

Merge requests are very welcome! For major changes, please open an issue first to discuss what you would like to change.
All commits should use the Angular commit scheme:

```
<type>(<scope>): <short summary>
  │       │             │
  │       │             └─⫸ Summary in present tense. Not capitalized. No period at the end.
  │       │
  │       └─⫸ Commit Scope: animations|bazel|benchpress|common|compiler|compiler-cli|core|
  │                          elements|forms|http|language-service|localize|platform-browser|
  │                          platform-browser-dynamic|platform-server|router|service-worker|
  │                          upgrade|zone.js|packaging|changelog|docs-infra|migrations|ngcc|ve|
  │                          devtools
  │
  └─⫸ Commit Type: build|ci|docs|feat|fix|perf|refactor|test
```

The `<type>` and `<summary>` fields are mandatory, the `(<scope>)` field is optional.
##### Type

Must be one of the following:

* **build**: Changes that affect the build system or external dependencies (example scopes: gulp, broccoli, npm)
* **ci**: Changes to our CI configuration files and scripts (examples: CircleCi, SauceLabs)
* **docs**: Documentation only changes
* **feat**: A new feature
* **fix**: A bug fix
* **perf**: A code change that improves performance
* **refactor**: A code change that neither fixes a bug nor adds a feature
* **test**: Adding missing tests or correcting existing tests


Please make sure to update tests as necessary.

## License

[BSD-3-Clause](https://choosealicense.com/licenses/bsd-3-clause/)
highway-env-iPLAN 0.0.1
Codebase for for our version of highway-way modified for iPLAN paper

# highway-env for iPLAN
Our forked version of [highway-env](https://github.com/Farama-Foundation/HighwayEnv) used in our paper:

[iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://arxiv.org/abs/2306.06236)

More details could be found in the main page of [highway-env](https://github.com/Farama-Foundation/HighwayEnv)
and [our project](https://github.com/wuxiyang1996/iPLAN).

# Major Changes
* Add two behavior-driven vehicle models, `DefensiveVehicle` and `AggressiveVehicle` in `vehicle/behavior.py`.
* Add multi-agent support for `Highway` scenario given in `envs/highway_env.py`, modify the `MultiAgentWrapper`
in `vehicle/common/abstract.py`.
* Add three heterogeneous traffic scenarios, `HighwayEnvHetero`, `HighwayEnvHetero_H` and `HighwayEnvHetero_VH`
in `envs/highway_env.py`, with vehicle ID broadcasting and different behavior-driven vehicles.
* Add multi-agent support for visualization in `Highway` scenario that allows a camera following each agent
and visualize their surroundings from their respective viewpoints.

# Animation
The animation shows 5 such learning agents (Green) with their surroundings from their respective viewpoints. 
Behavior-driven vehicles in the environment include: Normal (Blue), Aggressive (Purple) and Defensive (Yellow).
Vehicles terminate (Red) when colliding with other vehicles.

<p align="center">
    <img src="animation/iPLAN_Hetero_H_5_90.0_21.81.gif"><br/>
    <em> iPLAN in chaotic (hard) scenario of Heterogeneous Highway 
    (Num of agents succeed: 5, Avg. survival time: 90, Avg. speed: 21.81).</em>
</p>