hybrid-shoot 0.2.0

A custom RL environment with C++ backend and Gymnasium wrapper.

Hybrid Shoot Environment

This environment is designed as a sanity check for reinforcement learning with hybrid action spaces (discrete + continuous). It supports both Gymnasium (single agent with hybrid actions) and PettingZoo (multi-agent decomposition).

There are num_enemies enemies in a 2D space. The goal is to Jam and Shoot them.

Installation

pip install .

Gymnasium Usage

The Gymnasium environment presents a single agent with a Tuple action space.

from hybrid_shoot import HybridShootEnv

env = HybridShootEnv()
obs, info = env.reset()
# Action: (Jam_Target_Index, [Shoot_X, Shoot_Y])
action = (0, [0.5, 0.5]) 
obs, reward, done, truncated, info = env.step(action)

Action Space (Gymnasium)

A spaces.Tuple containing:

1. Jam: Discrete(num_enemies) - Selects which enemy to jam.
2. Shoot: Box(low=0, high=map_size, shape=(2,)) - [x, y] coordinates to shoot at.

PettingZoo Usage

The PettingZoo environment decomposes the task into two cooperating agents.

from hybrid_shoot import HybridShootPettingZooEnv

env = HybridShootPettingZooEnv()
observations, infos = env.reset()

Agents & Action Spaces (PettingZoo)

1. jammer: Discrete(num_enemies) - Selects which enemy to jam. (jammed enemy does not cause negative reward this turn)
2. shooter: Box(low=0, high=map_size, shape=(2,)) - Selects the [x, y] coordinates to shoot.

Game Mechanics

Jamming: Stops the targeted enemy from dealing damage this turn. Shooting: Fires at location (x, y).

• Standard Mode (independent_mode=False): An enemy must be jammed to be vulnerable to being shot. Shooting an unjammed enemy does nothing.
• Independent Mode (independent_mode=True): Jamming prevents damage, and Shooting kills enemies regardless of whether they are jammed.