This repository provided an UAV target assignment RL environment modelled in Agent-Environment-Cycle (AEC). The environment simulates a scenario where multiple drones can be assigned to various threats, each with different threat levels.

In this task allocation problem:

• There may have a variable number of drones (agents) and threats.
• Each threat has a certain threat level, represented by a probability.
• Drones should be allocated to threats to neutralize them.
• The goal is to optimize the allocation of drones to threats to maximize overall performance.

The environment is designed using the PettingZoo library's AEC paradigm. Key features include:

1. Variable number of agents: The number of drones can vary between episodes.
2. Threat representation: Threats are represented by their threat levels (probabilities).
3. Action space: Each drone can choose which threat to engage.
4. Observation space: Agents observe the threat levels, pre-calculated allocations, and current allocations.
5. Reward structure: Rewards are based on threat coverage, success rate, and other factors.

• TaskAllocationEnv: The main environment class.
• reset(): Initializes the environment for a new episode.
• step(): Processes agent actions and updates the environment state.
• observe(): Returns the current observation for an agent.
• render(): Visualizes the current state of the environment.

The environment includes a detailed visualization system using Pygame. It displays:

• Threat levels (color-coded)
• Actual threats
• Drone allocations
• Eliminated threats
• Various statistics and information

To use this environment:

1. pip install -r requirements.txt
2. Import the environment:

from task_allocation_env import raw_env

env = raw_env()