semvec/pypi-packages huggingface dataset:
EnvQuest 0.0.1
A collection of Reinforcement Learning algorithms to train autonomous agents in different environments.
Requires Python: >=3.8

# RL Studio
Play with reinforcement learning algorithms.

## Requirements
You need these requirements to run the project:
* python >= 3.8
* [uv](https://docs.astral.sh/uv/getting-started/installation/)
* ffmpeg
```shell
# Linux
sudo apt install ffmpeg
# MacOS
brew install ffmpeg
```


## Installation

Install the python dependencies
```shell
uv venv .venv
source .venv/bin/activate
uv sync --frozen
```

Start a local WandB server to track your experiments
```shell
wandb server start
```

## Usage

### Run a random policy agent
Run a random agent in a Gym environment 
```shell
python -m scripts.run_random_agent
```

Read the python script for more information: [scripts/run_random_agent.py](scripts/run_random_agent.py)

### Train Agents

#### DQN Agent
Train a DQN policy agent in a Gym environment
```shell
python -m scripts.train_dqn
```

Read the python script for more information: [scripts/train_dqn.py](scripts/train_dqn.py)


#### Sarsa Agent
Train a Sarsa policy agent in a Gym environment
```shell
python -m scripts.train_sarsa
```

Read the python script for more information: [scripts/train_sarsa.py](scripts/train_sarsa.py)
trainboxxxxxxx 0.0.1
A collection of Reinforcement Learning algorithms to train autonomous agents in different environments.
Requires Python: >=3.8

# RL Studio
Play with reinforcement learning algorithms.

## Requirements
You need these requirements to run the project:
* python >= 3.8
* [uv](https://docs.astral.sh/uv/getting-started/installation/)
* ffmpeg
```shell
# Linux
sudo apt install ffmpeg
# MacOS
brew install ffmpeg
```


## Installation

Install the python dependencies
```shell
uv venv .venv
source .venv/bin/activate
uv sync --frozen
```

Start a local WandB server to track your experiments
```shell
wandb server start
```

## Usage

### Run a random policy agent
Run a random agent in a Gym environment 
```shell
python -m scripts.run_random_agent
```

Read the python script for more information: [scripts/run_random_agent.py](scripts/run_random_agent.py)

### Train Agents

#### DQN Agent
Train a DQN policy agent in a Gym environment
```shell
python -m scripts.train_dqn
```

Read the python script for more information: [scripts/train_dqn.py](scripts/train_dqn.py)


#### Sarsa Agent
Train a Sarsa policy agent in a Gym environment
```shell
python -m scripts.train_sarsa
```

Read the python script for more information: [scripts/train_sarsa.py](scripts/train_sarsa.py)
basis-vm 0.1.2
A complete Python virtual machine for executing smart contracts.
Requires Python: >=3.9

# Running Tests

To run the tests for this project, you need to use the `make test` command. Before running the tests, ensure that the path to the virtual environment is correctly configured.

## Steps to Run Tests

1. **Configure the Virtual Environment Path:**
    Open the `Makefile` and set the `VENV_PATH` variable to the path of your virtual environment. For example:
    ```makefile
    VENV_PATH=~/Desktop/Environments/base_computing/micro_dlts_chain/smart_contracts_client/bin/activate
    ```

2. **Run the Tests:**
    Execute the following command in your terminal:
    ```sh
    make test
    ```

    This command will:
    - Activate the virtual environment.
    - Run the tests using `unittest` framework.

## Steps to Generate Coverage Report

1. **Run the Coverage Command:**
    Execute the following command in your terminal:
    ```sh
    make coverage
    ```

    This command will:
    - Activate the virtual environment.
    - Run the tests and measure code coverage.
    - Generate a coverage report.
    - Generate an HTML report in the `htmlcov` directory.

## Example

Here is an example of the `Makefile` configuration:
```makefile
VENV_PATH=~/Desktop/Environments/base_computing/micro_dlts_chain/smart_contracts_client/bin/activate

.PHONY: test coverage

test:
    source $(VENV_PATH) && python -m unittest discover -s tests

coverage:
    source $(VENV_PATH) && coverage run -m unittest discover -s tests
    coverage report
    coverage html
    echo "Informe HTML generado en la carpeta 'htmlcov'."
```

Make sure the `VENV_PATH` points to the correct location of your virtual environment's `activate` script.