failure-api 0.1.6

A modular communication failure API for Multi-Agent Reinforcement Learning environments

failure-api

A modular failure injection API for Multi-Agent Reinforcement Learning (MARL) environments.
Built on top of PettingZoo, this package introduces wrappers that simulate communication failures and observation noise between agents — enabling robust training under partial observability and degraded communication.

Installation

pip install failure-api

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Components

Wrappers

Wrapper	Purpose
BaseWrapper	Provides utility functions such as RNG seeding.
SharedObsWrapper	Enables shared observations across agents; used internally.
CommunicationWrapper	Dynamically masks agent observations/actions based on failures.
NoiseWrapper	Injects noise into shared observations, excluding already masked values.

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Communication Models

Model	Description
ProbabilisticModel	Drops communication links using Bernoulli sampling with fixed probability.
BaseMarkovModel	Models temporally correlated failures with a Markov chain.
DistanceModel	Drops communication based on exceeding a distance threshold.
DelayBasedModel	Delays messages with probabilistic delivery and expiration.
SignalBasedModel	Applies degradation using inverse-square signal loss and random drops.

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Noise Models

Model	Description
GaussianNoiseModel	Adds Gaussian noise: X + N(μ, σ²); useful for random sensor noise.
LaplacianNoiseModel	Adds Laplacian noise: X + Laplace(μ, b); good for sparse spikes.
CustomNoise	Accepts any callable function for custom distortion logic.

All models inherit from the NoiseModel base class and implement .apply().

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Example Usage

from mpe2 import simple_spread_v3
from failure_api.wrappers import CommunicationWrapper, NoiseWrapper
from failure_api.communication_models import ProbabilisticModel, BaseMarkovModel
from failure_api.noise_models import GaussianNoiseModel
from pettingzoo.utils import aec_to_parallel

# Base PettingZoo environment
env = simple_spread_v3.env(N=3, max_cycles=25)
agent_ids = env.possible_agents

# Apply communication failure model
model = ProbabilisticModel(agent_ids=agent_ids, failure_prob=0.8)
wrapped_env = CommunicationWrapper(env, failure_models=[model])

# Convert to parallel API
parallel_env = aec_to_parallel(wrapped_env)

# Run Simulation
observations, _  = parallel_env.reset(seed=42)
initial_comm_matrix = wrapped_env.get_communication_state().astype(int)
print(f"\nInitial Communication Matrix (0=masked, 1=visible)\n")
print(initial_comm_matrix)

for _ in range(10):
    actions = {agent: parallel_env.action_space(agent).sample() for agent in parallel_env.agents}
    observations, rewards, terminations, truncations, infos = parallel_env.step(actions)
    print(f"Masked State: (0=masked, 1=visible)\n", wrapped_env.get_communication_state())
    
    if all(terminations.values()) or all(truncations.values()):
        break

    
#%%
# OPTIONAL: Noise model, can be used with/without Communication wrapper
#  to compare observations before noise is injected, the communication wrapper
# needs to be called to get the raw observation.


# Base PettingZoo environment
noise_env = simple_spread_v3.env(N=3, max_cycles=25)
agent_ids = env.possible_agents

comm_wrapper = CommunicationWrapper(noise_env, failure_models=[ProbabilisticModel(
    agent_ids, failure_prob=0.8
)]) # step will not be taken so as not to mask observations 

# convert to parallel env
parallel_comm_wrapper = aec_to_parallel(comm_wrapper)
comm_obs, _ = parallel_comm_wrapper.reset(seed=42)
comm_obs_agent_0 = comm_obs[agent_ids[0]]

# Inject Noise 
noise_wrapper = NoiseWrapper(comm_wrapper, noise_model=
                             GaussianNoiseModel(mean=0.1, std=0.2 ))

# Convert to parallel API
noise_parallel_env = aec_to_parallel(noise_wrapper)

# Run Simulation
noisy_obs, _  = noise_parallel_env.reset(seed=42)

noisy_obs_agent_0 = noisy_obs[agent_ids[0]]

print("\nComparison for agent_0")
for other_agent in comm_obs_agent_0:
    print(f"\nObservation from {other_agent}:")
    print(f" Clean:")
    print(f"{comm_obs_agent0[other_agent]:.3f}")
    
    print(f"\n Noisy :")
    print(f"{noisy_obs_agent0[other_agent]:.3f}")

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Custom Noise Example

from failure_api.noise_models import CustomNoise

def zero_every_other(obs, space=None):
    obs[::2] = 0
    return obs

noise = CustomNoise(noise_fn=zero_every_other)
env = NoiseWrapper(env, noise_model=noise)

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Features

• ✅ Pluggable communication failure and noise models
• 🔁 Compatible with both AEC and Parallel API in PettingZoo
• 🔒 Observation masking via active communication matrix
• 🎯 Observation noise excludes zero-masked (unseen) values
• 🤖 Supports MARL training frameworks (e.g. IQL, QMIX)
• 🧩 Fully extensible for custom failure scenarios

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API Highlights

CommunicationWrapper

• Applies observation/action masking using ActiveCommunication
• Works with any PettingZoo AEC environment
• Internally uses SharedObsWrapper
• Supports multi-model failure injection

NoiseWrapper

• Adds noise only to visible observations
• Maintains shape and structure of observation space
• Customizable with user-defined noise logic

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Requirements

• Python 3.8+
• pettingzoo
• gymnasium
• numpy
• scipy
• (Optional): matplotlib, tqdm, networkx

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Testing

Use this to inspect the communication mask during runtime:

print(env.get_communication_state())  # Visualize active communication links

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Extendability

You can define your own failure or noise model by subclassing:

from failure_api.communication_models import CommunicationModels

class MyFailureModel(CommunicationModels):
    def update_connectivity(self, comm_matrix):
        # Custom logic here
        return comm_matrix

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Citation

If you use this in academic work, please cite:

@bachelorsthesis{adegun2025marlapi,
  author    = {Oluwadamilare Israel Adegun},
  title     = {Development of a Multi-Agent Reinforcement Learning API for Dynamic Observation and Action Spaces},
  school    = {University of Duisburg-Essen},
  year      = {2025},
  note      = {[Online; accessed 21-May-2025]}
}

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🔗 Links

• 🔗 GitHub: https://github.com/dreyman91/FailureAPI
• 📦 PyPI: pypi.org/project/failure-api

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