Specification

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Import	from free_range_zoo.envs import wildfire_v0
Actions	Discrete & Stochastic
Observations	Discrete and fully Observed with private observations
Parallel API	Yes
Manual Control	No
Agent Names	[\(firefighter\)_0, …, \(firefighter\)_n]
# Agents	[0, \(n_{firefighters}\)]
Action Shape	(\(envs\), 2)
Action Values	[\(fight_0\), …, \(fight_{tasks}\), \(noop\) (-1)]
Observation Shape	TensorDict: {  self: \(<ypos, xpos, power, suppressant, range, capacity, equipment>\)  others: \(<ypos, xpos, [power], [suppressant], [range], [capacity], [equipment]>\)  tasks: \(<y, x, fire level, intensity>\) batch_size: \(num\_envs\) }
Observation Values	self:  \(ypos\): \([0, max_y]\)  \(xpos\): \([0, max_x]\)  \(power\): \([0, max_{effective\_power}]\)  \(suppressant\): \([0, max_{effective\_suppressant}]\)  \(range\): \([0, max_{effective\_range}]\)  \(capacity\): \([0, max_{effective\_capacity}]\)  \(equipment\): \([0, max_{equipment\_state}]\) others:  \(ypos\): \([0, max_y]\)  \(xpos\): \([0, max_x]\)  \([power]\): included if observe_other_power  \([suppressant]\): included if observe_other_suppressant  \([range]\): included if observe_other_range  \([capacity]\): included if observe_other_capacity  \([equipment]\): included if observe_other_equipment tasks  \(ypos\): \([0, max_y]\)  \(xpos\): \([0, max_x]\)  \(fire\_level\): \([0, max_{fire\_level}]\)  \(intensity\): \([0, num_{fire\_states}]\)

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Usage

Parallel API

from free_range_zoo.envs import wildfire_v0

main_logger = logging.getLogger(__name__)

# Initialize and reset environment to initial state
env = wildfire_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 wildfire_v0

main_logger = logging.getLogger(__name__)

# Initialize and reset environment to initial state
env = wildfire_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()

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Configuration

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API