Generals.io Bots

Installation • Getting Started • Customization • Environment • Wiki

Generals-bots is a fast-paced strategy environment where players compete to conquer their opponents' generals on a 2D grid. While the goal is simple — capture the enemy general — the gameplay combines strategic depth with fast-paced action, challenging players to balance micro and macro-level decision-making. The combination of these elements makes the game highly engaging and complex.

Highlights:

• ⚡ blazing-fast simulator: run thousands of steps per second with numpy-powered efficiency
• 🤝 seamless integration: fully compatible with RL standards 🤸Gymnasium and 🦁PettingZoo
• 🔧 extensive customization: easily tailor environments to your specific needs
• 🚀 effortless deployment: launch your agents to generals.io
• 🔬 analysis tools: leverage features like replays for deeper insights

Note

This repository is based on the generals.io game (check it out, it's a lot of fun!). The one and only goal of this project is to provide a bot development platform, especially for Machine Learning based agents.

📦 Installation

You can install the latest stable version via pip for reliable performance

pip install generals-bots

or clone the repo for the most up-to-date features

git clone https://github.com/strakam/generals-bots
cd generals-bots
make install

Note

Under the hood, make install installs poetry and the package using poetry.

🌱 Getting Started

Creating an agent is very simple. Start by subclassing an Agent class just like RandomAgent or ExpanderAgent. You can specify your agent id (name) and color and the only thing remaining is to implement the act function, that has the signature explained in sections down below.

Usage Example (🤸 Gymnasium)

The example loop for running the game looks like this

import gymnasium as gym

from generals.agents import RandomAgent, ExpanderAgent

# Initialize agents
agent = RandomAgent()
npc = ExpanderAgent()

# Create environment
env = gym.make("gym-generals-v0", agent=agent, npc=npc, render_mode="human")

observation, info = env.reset()
terminated = truncated = False
while not (terminated or truncated):
    action = agent.act(observation)
    observation, reward, terminated, truncated, info = env.step(action)
    env.render()

Tip

Check out Wiki for more commented examples to get a better idea on how to start 🤗.

🎨 Custom Grids

Grids on which the game is played on are generated via GridFactory. You can instantiate the class with desired grid properties, and it will generate grid with these properties for each run.

import gymnasium as gym
from generals import GridFactory

grid_factory = GridFactory(
    grid_dims=(10, 10),                    # Dimensions of the grid (height, width)
    mountain_density=0.2,                  # Probability of a mountain in a cell
    city_density=0.05,                     # Probability of a city in a cell
    general_positions=[(0,3),(5,7)],       # Positions of generals (i, j)
)

# Create environment
env = gym.make(
    "gym-generals-v0",
    grid_factory=grid_factory,
    ...
)

You can also specify grids manually, as a string via options dict:

import gymnasium as gym

env = gym.make("gym-generals-v0", ...)
grid = """
.3.#
#..A
#..#
.#.B
"""

options = {"grid": grid}

# Pass the new grid to the environment (for the next game)
env.reset(options=options)

Grids are created using a string format where:

• . represents passable terrain
• # indicates impassable mountains
• A, B mark the positions of generals
• digits 0-9 represent cities, where the number specifies amount of neutral army in the city, which is calculated as 40 + digit

🔬 Interactive Replays

We can store replays and then analyze them in an interactive fashion. Replay class handles replay related functionality.

Storing a replay

import gymnasium as gym

env = gym.make("gym-generals-v0", ...)

options = {"replay": "my_replay"}
env.reset(options=options) # The next game will be encoded in my_replay.pkl

Loading a replay

from generals import Replay

# Initialize Replay instance
replay = Replay.load("my_replay")
replay.play()

🕹️ Replay controls

You can control your replays to your liking! Currently, we support these controls:

• q — quit/close the replay
• r — restart replay from the beginning
• ←/→ — increase/decrease the replay speed
• h/l — move backward/forward by one frame in the replay
• spacebar — toggle play/pause
• mouse click on the player's row — toggle the FoV (Field of View) of the given player

Warning

We are using the pickle module which is not safe! Only open replays you trust.

🌍 Environment

🔭 Observation

An observation for one agent is a dictionary {"observation": observation, "action_mask": action_mask}.

The observation is a Dict. Values are either numpy matrices with shape (N,M), or simple int constants:

Key	Shape	Description
armies	(N,M)	Number of units in a visible cell regardless of the owner
generals	(N,M)	Mask indicating visible cells containing a general
cities	(N,M)	Mask indicating visible cells containing a city
mountains	(N,M)	Mask indicating visible cells containing mountains
neutral_cells	(N,M)	Mask indicating visible cells that are not owned by any agent
owned_cells	(N,M)	Mask indicating visible cells owned by the agent
opponent_cells	(N,M)	Mask indicating visible cells owned by the opponent
fog_cells	(N,M)	Mask indicating fog cells that are not mountains or cities
structures_in_fog	(N,M)	Mask showing cells containing either cities or mountains in fog
owned_land_count	—	Number of cells the agent owns
owned_army_count	—	Total number of units owned by the agent
opponent_land_count	—	Number of cells owned by the opponent
opponent_army_count	—	Total number of units owned by the opponent
timestep	—	Current timestep of the game
priority	—	1 if your move is evaluted first, 0 otherwise

The action_mask is a 3D array with shape (N, M, 4), where each element corresponds to whether a move is valid from cell [i, j] in one of four directions: 0 (up), 1 (down), 2 (left), or 3 (right).

⚡ Action

Actions are in a dict format with the following key: value format:

• pass indicates whether you want to 1 (pass) or 0 (play).
• cell value is an np.array([i,j]) where i,j are indices of the cell you want to move from
• direction indicates whether you want to move 0 (up), 1 (down), 2 (left), or 3 (right)
• split indicates whether you want to 1 (split) units and send only half, or 0 (no split) where you send all units to the next cell

Tip

You can see how actions and observations look like by printing a sample form the environment:

print(env.observation_space.sample())
print(env.action_space.sample())

🎁 Reward

It is possible to implement custom reward function. The default reward is awarded only at the end of a game and gives 1 for winner and -1 for loser, otherwise 0.

def custom_reward_fn(observation, action, done, info):
    # Give agent a reward based on the number of cells they own
    return observation["observation"]["owned_land_count"]

env = gym.make(..., reward_fn=custom_reward_fn)
observations, info = env.reset()

🚀 Deployment to Live Servers

Complementary to local development, it is possible to run agents online against other agents and players. We use socketio for communication, and you can either use our autopilot to run agent in a specified lobby indefinitely, or create your own connection workflow. Our implementations expect that your agent inherits from the Agent class, and has implemented the required methods.

from generals.remote import autopilot
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--user_id", type=str, default=...) # Register yourself at generals.io and use this id
parser.add_argument("--lobby_id", type=str, default=...) # The last part of the lobby url
parser.add_argument("--agent_id", type=str, default="Expander") # agent_id should be "registered" in AgentFactory

if __name__ == "__main__":
    args = parser.parse_args()
    autopilot(args.agent_id, args.user_id, args.lobby_id)

This script will run ExpanderAgent in the specified lobby.

🙌 Contributing

You can contribute to this project in multiple ways:

• 🤖 If you implement ANY non-trivial agent, send it to us! We will publish it, so others can play against it.
• 💡 If you have an idea on how to improve the game, submit an issue or create a PR, we are happy to improve! We also have some ideas (see issues), so you can see what we plan to work on.

Tip

Check out wiki to learn in more detail on how to contribute.