# target_assign_rl

**Repository Path**: Morphlng/target_assign_rl

## Basic Information

- **Project Name**: target_assign_rl
- **Description**: 多目标分配任务的强化学习训练环境
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2024-11-04
- **Last Updated**: 2025-04-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# AEC Reinforcement Learning Environment for Drone Task Allocation

![Task Allocation Environment](docs/images/target_assign.gif)

## Overview

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.

## Problem Description

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.

## Environment Design

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.

## Key Components

- `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.

## Visualization

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

## Usage

To use this environment:

1. `pip install -e .`
2. Import the environment:

```python
from target_assign_rl import raw_env

env = raw_env() # AEC environment

# or

from target_assign_rl import TaskAllocationEnv

env = TaskAllocationEnv() # Gym environment
```