R2 See, Do - a journey in deep reinforcement learning.

• Notebooks
• Project Setup
• RL Environments
• Running Tests

r2seedo uses hatch for project management. You'll need it installed (ideally in an isolated environment) before setting up r2seedo.

r2seedo depends on gymnasium, which is best installed with conda / mamba, thus hatch-conda is also recommended.

# Update mamba
mamba update -n base mamba

# Update base environment packages 
mamba update -n base --all

# Install hatch & hatchling
mamba install -n base -c conda-forge hatch hatchling

# Install hatch-conda
mamba activate base && python -m pip install hatch-conda

git clone [email protected]:libertininick/r2seedo.git

hatch will install r2seedo in development mode along with its development dependencies inside of a virtual environment managed by hatch.

# Navigate to root project directory
cd r2seedo

# (optional) if using conda / mamba envs activate the base environment
mamba activate base

# Create default environment
hatch env create

Table of Contents

• RL environment packages are best installed in independent virtual environment
• Each RL environment used in r2seedo has its own virtual environment configuration defined in pyproject.toml
• An environment can be created using hatch env create <env name> :

NOTE: Make sure the base environment is activated before creating a new environment: mamba activate base

Table of Contents

Run tests and coverage report using hatch

hatch run default:test-cov

Table of Contents