Specification

---

Import	from free_range_zoo.envs import cybersecurity_v0
Actions	Discrete & Stochastic
Observations	Discrete and Partially Observed with Private Observations
Parallel API	Yes
Manual Control	No
Agent Names	[\(attacker_0\), …, \(attacker_n\), \(defender_0\), …, \(defender_n\)]
# Agents	[0, \(n_{attackers}\) + \(n_{defenders}\)]
Action Shape	(\(envs\), 2)
Action Values	Attackers: [\(attack_0\), …, \(attack_{tasks}\), \(noop\) (-1)] Defenders: [\(move_0\), …, \(move_{tasks}\), \(noop\) (-1), \(patch\) (-2), \(monitor\) (-3)]
Observation Shape	Attackers: TensorDict {  self: \(<power, presence>\)  others: \(<power, presence>\)  tasks: \(<state>\) batch_size: \(num\_envs\) } Defenders: TensorDict {  self: \(<power, presence, location>\)  others: \(<power, presence, location>\)  tasks: \(<state>\) batch_size: \(num\_envs\)}
Observation Values	Attackers:  self   \(power\): [\(0\), \(max\_power_{attacker}\)]   \(presence\): [\(0\), \(1\)]  others   \(power\): [\(0\), \(max\_power_{attacker}\)]   \(presence\): [\(0\), \(1\)]  tasks   \(state\): [\(0\), \(n_{network\_states}\)] Defenders:  self   \(power\): [\(0\), \(max\_power_{defender}\)]   \(presence\): [\(0\), \(1\)]   \(location\): [\(0\), \(n_{subnetworks}\)]  others   \(power\): [\(0\), \(max\_power_{defender}\)]   \(presence\): [\(0\), \(1\)]   \(location\): [\(0\), \(n_{subnetworks}\)]  tasks   \(state\): [\(0\), \(n_{network\_states}\)]

---

Usage

Parallel API

from free_range_zoo.envs import cybersecurity_v0

main_logger = logging.getLogger(__name__)

# Initialize and reset environment to initial state
env = cybersecurity_v0.parallel_env(render_mode="human")
observations, infos = env.reset()

# Initialize agents and give initial observations
agents = []

cumulative_rewards = {agent: 0 for agent in env.agents}

current_step = 0
while not torch.all(env.finished):
    agent_actions = {
        agent_name: torch.stack([agents[agent_name].act()])
        for agent_name in env.agents
    }  # Policy action determination here

    observations, rewards, terminations, truncations, infos = env.step(agent_actions)
    rewards = {agent_name: rewards[agent_name].item() for agent_name in env.agents}

    for agent_name, agent in agents.items():
        agent.observe(observations[agent_name][0])  # Policy observation processing here
        cumulative_rewards[agent_name] += rewards[agent_name]

    main_logger.info(f"Step {current_step}: {rewards}")
    current_step += 1

env.close()

AEC API

from free_range_zoo.envs import cybersecurity_v0

main_logger = logging.getLogger(__name__)

# Initialize and reset environment to initial state
env = cybersecurity_v0.parallel_env(render_mode="human")
observations, infos = env.reset()

# Initialize agents and give initial observations
agents = []

cumulative_rewards = {agent: 0 for agent in env.agents}

current_step = 0
while not torch.all(env.finished):
    for agent in env.agent_iter():
        observations, rewards, terminations, truncations, infos = env.last()

        # Policy action determination here
        action = env.action_space(agent).sample()

        env.step(action)

    rewards = {agent: rewards[agent].item() for agent in env.agents}
    cumulative_rewards[agent] += rewards[agent]

    current_step += 1
    main_logger.info(f"Step {current_step}: {rewards}")

env.close()

---

Configuration

---

API