# TrashPickup Environment

A lightweight multi-agent reinforcement learning (RL) environment designed for coordination and cooperation research. Agents pick up trash and deposit it in bins for rewards.

## Key Features
- **Multi-Agent Coordination:** Encourages teamwork, efficient planning, and resource allocation.
- **Configurable Setup:** Adjustable grid size, number of agents, trash, bins, and episode length.
- **Discrete Action Space:** Actions include `UP`, `DOWN`, `LEFT`, `RIGHT`.
- **Fast and Lightweight:** Optimized for rapid training and testing.

## Example Research Goals
- Investigate emergent behaviors like task allocation and coordination.
- Study efficient resource collection and bin-pushing strategies.

## Ideal For
- RL researchers exploring multi-agent cooperation.
- Students learning about multi-agent systems.
- Developers testing scalable RL algorithms.
