sheep-herding-rl 1.0.1

A reinforcement learning environment for sheep herding simulation with PPO, SAC, and TD3 algorithms

Sheep Herding RL

A reinforcement learning environment for sheep herding simulation with multiple agent implementations including PPO, SAC, and TD3 algorithms.

Description

This package provides a pygame-based simulation environment where agents (dogs and wolves) interact with sheep in a herding scenario. The environment supports both local ego-centric and global observation modes, making it suitable for various reinforcement learning experiments.

Features

• Multi-Agent Environment: Supports dog (herder) and wolf (predator) agents
• Multiple RL Algorithms: Implementations of PPO, SAC, and TD3
• Flexible Observation Modes:
  • Local ego-centric grid observations
  • Global state observations
• Configurable Parameters: Easy-to-modify configuration system
• Visualization: Real-time rendering with debug modes
• Training Framework: Built-in trainers for on-policy and off-policy algorithms

Installation

From Source

git clone https://github.com/dzijo/ferit-hackathon.git
cd ferit-hackathon
pip install -e .

With Development Dependencies

pip install -e ".[dev]"

Dependencies

• Python >= 3.8
• PyTorch >= 2.0.0
• pygame
• numpy
• pillow
• scipy
• pyyaml
• matplotlib

Project Structure

sheep-herding-rl/
├── agents/              # Base agent classes
├── algorithms/          # RL algorithm implementations
│   ├── ppo/            # Proximal Policy Optimization
│   ├── sac/            # Soft Actor-Critic
│   └── td3/            # Twin Delayed DDPG
├── sim/                # Simulation environment
│   ├── environment.py  # Main environment class
│   ├── dog.py          # Dog agent
│   ├── wolf.py         # Wolf agent
│   └── sheep.py        # Sheep entities
├── trainers/           # Training utilities
├── utils/              # Helper utilities
├── config.py           # Configuration parameters
├── simulator.py        # Main simulator class
└── actions.py          # Action definitions

Quick Start

from sheep_herding_rl import Simulator
from sheep_herding_rl import config

# Create a simulator instance
sim = Simulator()

# Run a step
state = sim.get_state()
dog_obs = sim.get_dog_observation()
wolf_obs = sim.get_wolf_observation()

# Take actions
dog_action = [0.5, 0.0]  # [forward_speed, turn_rate]
wolf_action = [0.3, 0.1]
sim.step(dog_action, wolf_action)

# Get rewards
dog_reward = sim.get_dog_reward()
wolf_reward = sim.get_wolf_reward()

Configuration

All simulation parameters can be modified in config.py:

• Observation parameters: Grid size, range, channels
• Screen dimensions: Width, height
• Debug modes: Visualization options
• Splatting methods: Gaussian, bilinear, or discrete

Training

The package includes trainers for different algorithm types:

from trainers.on_policy_trainer import OnPolicyTrainer
from algorithms.ppo.agent import PPOAgent

# Create and train an agent
agent = PPOAgent(obs_dim, action_dim)
trainer = OnPolicyTrainer(agent, sim)
trainer.train(num_episodes=1000)

Algorithms

PPO (Proximal Policy Optimization)

On-policy algorithm with clipped objective for stable training.

SAC (Soft Actor-Critic)

Off-policy algorithm with entropy regularization for exploration.

TD3 (Twin Delayed DDPG)

Off-policy algorithm with twin critics and delayed policy updates.

License

MIT License - See LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Citation

If you use this package in your research, please cite:

@software{sheep_herding_rl,
  title = {Sheep Herding RL: A Multi-Agent Reinforcement Learning Environment},
  author = {ferip},
  year = {2025},
  url = {https://github.com/dzijo/ferit-hackathon}
}

Acknowledgments

Created for the FERIT Hackathon 2025.