multi-agent-coverage 0.2.0

High-performance batched multi-agent environment

Multi-Agent Coverage Environment

A high-performance batched multi-agent environment built with C++ (pybind11) and OpenMP for fast parallel simulation of agents exploring a 32×32 grid world with configurable danger maps. Supports two communication modes: partial-observability (radio) and global-communication.

Demo

Features

• High-Performance: ~290k FPS (partial-obs) / ~325k FPS (global-comms) for 16 parallel environments
• Dual Communication Modes: Partial-observability with radio or full global communication
• Batched Simulation: Run multiple independent environments efficiently in parallel via OpenMP
• Zero-Copy Memory: Direct memory sharing between C++ backend and PyTorch tensors
• Gymnasium Compatible: Standard gym.vector.VectorEnv interface
• Custom Maps: Load PNG/JPG/BMP or raw binary danger maps; auto-conversion built in
• Gravity-Based Attractions: Query attraction vectors towards 17 different map features for each agent
• PyGame Visualization: Real-time rendering of environment state with fog-of-war

Installation

From Source

# Clone repository
git clone <repository>
cd craptop

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
pip install -e .

Requirements

• Python 3.10+
• pybind11 ≥ 2.6
• Pillow (for PNG map conversion)
• MSVC (Windows) or GCC/Clang (Linux) with OpenMP support

Project Structure

src/
  gravity.h              # Shared header: constants, enums, gravity helpers
  batched_env.cpp        # Partial-obs mode  → _core module
  batched_env_global.cpp # Global-comms mode → _core_global module
multi_agent_coverage/
  __init__.py            # Re-exports BatchedEnvironment + BatchedEnvironmentGlobal
  env_wrapper.py         # Gymnasium wrapper (BatchedGridEnv)
setup.py                 # Builds both C++ extensions

Communication Modes

	Partial-Obs (Radio)	Global-Comms
Backend module	_core	_core_global
State stride	19,496 floats	8,200 floats
Observed danger	Per-agent (4 × 1024)	Shared (1 × 1024)
Observation mask	Per-agent (4 × 1024)	Shared (1 × 1024)
Expected obs	Per-agent belief state	N/A (matches obs)
Last agent locations	Per-agent belief about others	N/A (true positions known)
communication_prob	Controls radio position updates	Ignored
Use case	Decentralized / partial info	Centralized / full info

Maps

The environment supports two map inputs per environment:

Argument	Purpose
maps	Ground-truth danger map — what the environment actually uses for movement penalties and rewards
expected_maps	Prior belief map (e.g. satellite imagery) — used as the agents' initial expected danger before any exploration

Map Format

Maps are stored as raw binary float32 files (.bin) containing 1024 values (32×32 grid, row-major) in the range [-1.0, 1.0]:

• -1.0 → completely safe
• 0.0 → neutral
• +1.0 → maximum danger

PNG / Image Maps

Any PNG, JPG, or BMP image can be passed directly — the wrapper auto-converts it:

1. Converts to grayscale
2. Resizes to 32×32 with Lanczos resampling
3. Normalizes pixel values from [0, 255] → [-1.0, 1.0]
4. Saves a .bin sidecar file next to the image

Light pixels (255) map to +1.0 (danger); dark pixels (0) map to -1.0 (safe).

Converting Maps Manually

from multi_agent_coverage.env_wrapper import convert_map

# Convert a PNG to a .bin file (saved alongside the image)
bin_path = convert_map("my_map.png")            # -> "my_map.bin"
bin_path = convert_map("my_map.png", "out.bin") # explicit output path

Or use the standalone script:

python map_converter.py

Creating Maps Programmatically

import numpy as np

# 32x32 map: danger concentrated in a circle in the center
y, x = np.mgrid[0:32, 0:32]
dist = np.sqrt((y - 16)**2 + (x - 16)**2)
danger = np.clip(1.0 - dist / 16.0, -1.0, 1.0).astype(np.float32)
danger.flatten().tofile("circle_danger.bin")

API Reference

BatchedGridEnv

High-level gymnasium-compatible wrapper around the C++ environment.

Constructor

from multi_agent_coverage.env_wrapper import BatchedGridEnv, FeatureType

env = BatchedGridEnv(
    num_envs=16,              # Number of parallel environments
    n_agents=4,               # Agents per environment (fixed at 4 in C++ backend)
    map_size=32,              # Grid size (fixed at 32x32)
    device='cpu',             # PyTorch device ('cpu' or 'cuda')
    render_mode=None,         # 'human' for pygame window, None for headless
    seed=42,                  # Random seed for procedural map generation
    communication_prob=-1.0,  # Probability [0,1] of radio updates; -1 disables (partial-obs only)
    maps=None,                # str path or list of str paths to ground-truth maps
    expected_maps=None,       # str path or list of str paths to prior belief maps
    global_comms=False,       # If True, use global-communication backend
)

global_comms: When True, switches to the global-communication backend where all agents share a single observation mask and observed danger map, know each other's true positions, and require no expected_obs or last_agent_locations tracking. The state stride drops from 19,496 to 8,200 floats (~2.4× smaller), improving cache locality and throughput.

Map arguments accept:

• None — procedural sine/cosine map is generated per environment
• "map0.png" — same image used for all num_envs environments (auto-converted)
• "map0.bin" — same binary file used for all environments
• ["map0.bin", "map1.bin", ...] — one file per environment (list length must equal num_envs)

Methods

reset(seed=None, options=None)

Reset all environments and return observations.

obs, info = env.reset()
# obs: torch.Tensor of shape (num_envs, stride)
#   stride = 19496 (partial-obs) or 8200 (global-comms)

step(actions)

Execute actions and return observations, rewards, and terminal flags.

actions = np.random.uniform(-1, 1, (num_envs, n_agents, 2))  # or torch.Tensor
obs, rewards, terminated, truncated, info = env.step(actions)

# obs:        torch.Tensor (num_envs, stride)
# rewards:    torch.Tensor (num_envs, n_agents)
# terminated: torch.Tensor (num_envs,) bool — True when all cells discovered
# truncated:  torch.Tensor (num_envs,) bool — always False (no time limit)

In partial-obs mode, communication_prob (set in the constructor) is passed to the C++ step() to control probabilistic radio position updates between agents. In global-comms mode it is not used.

Environments that terminate are automatically reset at the start of their next step.

get_gravity_attractions(feature_type, agent_mask=None, pow=2, normalize=False, local=False)

Compute gravity attraction vectors for each agent towards cells of a given feature map.

The gravity force from each cell is: $\vec{F} = \text{mass} \cdot \hat{r} / r^{pow}$, summed over all cells (or only cells within view range when local=True).

from multi_agent_coverage.env_wrapper import FeatureType

gravity = env.get_gravity_attractions(
    feature_type=FeatureType.GLOBAL_UNDISCOVERED,
    agent_mask=None,   # None = all agents; or np.array([True, True, False, False])
    pow=2,             # Distance power exponent (1 = linear falloff, 2 = quadratic)
    normalize=False,   # If True, scale output so max vector norm = 1.0
    local=False,       # If True, only consider tiles/agents within VIEW_RANGE (7×7)
)
# Returns torch.Tensor of shape (num_envs, n_agents, 2) with (dy, dx) per agent

local mode: When local=True, gravity is computed only over the 7×7 tiles the agent can currently see (or nearby agents within view range). This avoids the global attractor/repulsor problem where agents get pulled to map edges or stuck in the center. Particularly useful for RECENCY_STALE anti-pheromone navigation.

Feature Types:

Feature Type	Description	Notes
EXPECTED_DANGER	Prior belief danger map (shared)
ACTUAL_DANGER	True ground-truth danger map (shared)
OBSERVED_DANGER	Observed danger (updated as cells are visited)	Per-agent in partial-obs; shared in global
OBS	Binary observation mask (1 = cell has been seen)	Per-agent in partial-obs; shared in global
EXPECTED_OBS	Agent's belief about what all agents have observed	Partial-obs only; aliases to OBS in global
GLOBAL_DISCOVERED	Global binary discovery map (union of all agents' obs)	Same as OBS in global mode
GLOBAL_UNDISCOVERED	Inverse of global discovery (attracts toward unseen cells)
OBS_UNDISCOVERED	Per-agent undiscovered cells	Aliases to GLOBAL_UNDISCOVERED in global
EXPECTED_OBS_UNDISCOVERED	Per-agent expected undiscovered cells	Aliases to GLOBAL_UNDISCOVERED in global
OTHER_AGENTS	Gravity from current positions of other agents
OTHER_AGENTS_LAST_KNOWN	Gravity from last known positions of other agents	Same as OTHER_AGENTS in global mode
RECENCY	Per-agent recency map — tiles in view set to 1.0 each frame, decay by ×0.99
RECENCY_STALE	Inverse of recency (1.0 − recency). Anti-pheromone effect
WALL_REPEL	Repelling force from map border walls
WALL_ATTRACT	Attracting force toward map border walls
GLOBAL_VORONOI_UNDISCOVERED	Voronoi-partitioned undiscovered tiles using oracle data (global_discovered + true agent positions)	Oracle info — suitable for reward shaping or centralized use; not epistemically correct for agent decisions in partial-obs
EXPECTED_VORONOI_UNDISCOVERED	Voronoi-partitioned undiscovered tiles using agent i's own belief (expected_obs[i] + last_agent_locations[i])	Epistemically correct for decentralized decision-making in partial-obs; identical to GLOBAL_VORONOI_UNDISCOVERED in global-comms mode

Voronoi / Territorial Gravity:

The two VORONOI_UNDISCOVERED variants assign each undiscovered tile to the nearest agent (Manhattan distance). Each agent only attracts toward the tiles in its own Voronoi territory, which prevents all agents from swarming toward the same final unexplored patch.

Variant	Discovery map	Agent positions used	Use case
GLOBAL_VORONOI_UNDISCOVERED	global_discovered (union of all agents' true obs)	True agent_locations	Reward shaping, centralized critics, global-comms mode
EXPECTED_VORONOI_UNDISCOVERED	expected_obs[i] (agent i's belief)	last_agent_locations[i] (last radio-reported positions)	Decentralized policy input in partial-obs

In global-comms mode both variants are identical (they both use the shared obs map and true positions). The distinction only matters in partial-obs mode, where GLOBAL_VORONOI_UNDISCOVERED leaks oracle information and should only be used outside the agent's policy network (e.g., to compute a shaped reward in the training loop).

# Partial-obs: give each agent its own territorial gravity (epistemically correct)
gravity = env.get_gravity_attractions(FeatureType.EXPECTED_VORONOI_UNDISCOVERED, pow=1)

# Use GLOBAL variant only for reward shaping (not as policy input)
global_gravity = env.get_gravity_attractions(FeatureType.GLOBAL_VORONOI_UNDISCOVERED, pow=1)

Agent Mask:

# Compute gravity only for the first two agents; others get zero vectors
mask = np.array([True, True, False, False])
gravity = env.get_gravity_attractions(FeatureType.GLOBAL_UNDISCOVERED, agent_mask=mask)

render()

Render the first environment to a pygame window. Called automatically each step when render_mode='human'.

The window shows:

• Black cells: undiscovered (fog of war)
• Green cells: discovered, safe (danger ≈ -1.0)
• Yellow cells: discovered, neutral danger (danger ≈ 0.0)
• Red cells: discovered, high danger (danger ≈ +1.0)
• Blue circles: agent positions with translucent 7×7 view-range boxes

env = BatchedGridEnv(num_envs=4, render_mode='human')
obs, _ = env.reset()

try:
    while True:
        actions = np.random.uniform(-1, 1, (4, 4, 2))
        env.step(actions)  # render() called automatically
except KeyboardInterrupt:
    env.close()

close()

Close the pygame window and release resources.

Usage Examples

Basic Loop

import numpy as np
from multi_agent_coverage.env_wrapper import BatchedGridEnv

env = BatchedGridEnv(num_envs=8, n_agents=4)
obs, _ = env.reset()

for step in range(1000):
    actions = np.random.uniform(-1, 1, (8, 4, 2))
    obs, rewards, terminated, truncated, info = env.step(actions)

env.close()

Global-Comms Mode

from multi_agent_coverage.env_wrapper import BatchedGridEnv

# Global communication — 2.4× smaller state, ~12% faster stepping
env = BatchedGridEnv(num_envs=16, global_comms=True)
obs, _ = env.reset()
print(f"Stride: {obs.shape[1]}")  # 8200 instead of 19496

for _ in range(1000):
    actions = np.random.uniform(-1, 1, (16, 4, 2))
    obs, rewards, terminated, truncated, info = env.step(actions)

env.close()

Partial-Obs with Radio Communication

from multi_agent_coverage.env_wrapper import BatchedGridEnv

# Agents update each other's positions with 30% probability per step
env = BatchedGridEnv(num_envs=16, communication_prob=0.3, global_comms=False)
obs, _ = env.reset()

for _ in range(1000):
    actions = np.random.uniform(-1, 1, (16, 4, 2))
    obs, rewards, terminated, truncated, info = env.step(actions)

env.close()

With Custom Maps

from multi_agent_coverage.env_wrapper import BatchedGridEnv

# Same map for all envs (PNG auto-converted to .bin on first run)
env = BatchedGridEnv(
    num_envs=8,
    maps="map0.png",
    expected_maps="expected_map0.png",
)

# Different maps per env
env = BatchedGridEnv(
    num_envs=2,
    maps=["map0.bin", "map1.bin"],
    expected_maps=["expected_map0.bin", "expected_map1.bin"],
)

Gravity-Based Navigation

from multi_agent_coverage.env_wrapper import BatchedGridEnv, FeatureType
import numpy as np

env = BatchedGridEnv(num_envs=16, maps="map0.png", expected_maps="expected_map0.png")
obs, _ = env.reset()

for step in range(1000):
    # Pull toward undiscovered areas, away from danger and other agents
    toward_unknown = env.get_gravity_attractions(FeatureType.GLOBAL_UNDISCOVERED, normalize=True, pow=1)
    avoid_danger   = env.get_gravity_attractions(FeatureType.OBSERVED_DANGER,     normalize=True, pow=2)
    spread_out     = env.get_gravity_attractions(FeatureType.OTHER_AGENTS,        normalize=True, pow=1)

    # Anti-pheromone: push agents away from areas they've recently visited
    leave_area     = env.get_gravity_attractions(FeatureType.RECENCY_STALE, normalize=True, pow=1, local=True)

    actions = toward_unknown - avoid_danger - spread_out + 0.3 * leave_area
    obs, rewards, terminated, truncated, info = env.step(actions)

env.close()

Observation Space Layout

Partial-Obs Mode (stride = 19,496)

Offset  | Size  | Content                  | Shape         | Range
--------|-------|--------------------------|---------------|----------
0       | 1024  | Expected Danger          | (32, 32)      | [-1, 1]
1024    | 1024  | Actual Danger            | (32, 32)      | [-1, 1]
2048    | 4096  | Observed Danger (×4)     | (4, 32, 32)   | [-1, 1]
6144    | 4096  | Observation Mask (×4)    | (4, 32, 32)   | {0, 1}
10240   | 8     | Agent Locations          | (4, 2)        | [0, 31] [y, x]
10248   | 4096  | Expected Obs (×4)        | (4, 32, 32)   | {0, 1}
14344   | 32    | Last Agent Locations     | (4, 4, 2)     | [0, 31]
14376   | 1024  | Global Discovered        | (32, 32)      | {0, 1}
15400   | 4096  | Recency (×4)             | (4, 32, 32)   | [0, 1]

• Observed Danger: Each agent's own observed danger map. Initialized from expected_danger, updated with actual_danger as tiles enter view.
• Expected Obs: Agent i's belief about what all agents have observed, based on last_agent_locations[i]. Cumulative (never cleared).
• Last Agent Locations: Agent i's last known position of agent j, updated when j is within view range or via radio communication.

Global-Comms Mode (stride = 8,200)

Offset  | Size  | Content                  | Shape         | Range
--------|-------|--------------------------|---------------|----------
0       | 1024  | Expected Danger          | (32, 32)      | [-1, 1]
1024    | 1024  | Actual Danger            | (32, 32)      | [-1, 1]
2048    | 1024  | Observed Danger (shared) | (32, 32)      | [-1, 1]
3072    | 1024  | Obs / Global Discovered  | (32, 32)      | {0, 1}
4096    | 8     | Agent Locations          | (4, 2)        | [0, 31] [y, x]
4104    | 4096  | Recency (×4)             | (4, 32, 32)   | [0, 1]

• All agents share a single observed danger map and observation mask.
• obs serves as the global discovered map (single source of truth).
• No expected_obs or last_agent_locations — agents know everything.

Recording Demonstrations

Generate an animated GIF matching the pygame renderer:

python gif.py

Performance

Benchmark results (16 parallel environments, 10k frames, Windows / MSVC with OpenMP):

Mode	Stride	Step FPS	Gravity Calls/s
Partial-obs	19,496	~290,000	~18,000
Global-comms	8,200	~325,000	~18,000

Global-comms mode is ~12% faster at stepping due to the smaller state stride and fewer per-step computations. Gravity performance is similar since it's dominated by the tile iteration, not state size.

Environment Details

State

• Map Size: 32×32 fixed grid
• Agents per Env: 4 fixed
• Agent Speed: 0.5 cells/step (reduced in danger zones)
• View Range: 3 cells (7×7 view window)
• Danger Scale: [-1.0, 1.0] — negative is safe, positive is dangerous
• Recency Decay: ×0.99 per frame

Rewards

Agents receive +1.0 reward (split equally among agents that can see the tile) for each newly discovered cell. A +10.0 bonus is awarded to all agents when all 1024 cells are discovered, ending the episode.

Dynamics

• Action vectors are L2-normalized before being applied
• Effective speed per step: SPEED × (1 - 0.8 × danger) at the agent's current cell
• Agent positions are clamped to [0, 31.99] on both axes
• Terminated environments auto-reset at the start of their next step() call

Building from Source

The package builds two C++ extensions:

Extension	Source	Purpose
_core	src/batched_env.cpp	Partial-obs mode
_core_global	src/batched_env_global.cpp	Global-comms mode

Both include the shared header src/gravity.h containing constants, the FeatureType enum, and all gravity computation functions.

# Install build dependencies
pip install pybind11 setuptools build pillow

# Install in editable mode (compiles both extensions)
pip install -e .

License

MIT