# StructuredEnv¶

class maze.core.env.structured_env.StructuredEnv

Interface for environments with sub-step structure, which is generally enough to cover multi-step, hierarchical and multi-agent environments.

This environment can continuously create and destroy a previously unknown, unlimited number of actors during the course of an episode. Every actor is associated with one of the available policies.

The lifecycle of the environment is decoupled from the lifecycle of the actors. The interaction loop should continue, until the environment as a whole is set to done, which is returned as usual by the step() function. Individual actors might end earlier, which can be queried by the is_actor_done() method.

Pseudo-code of the interaction loop:

# start a new episode observation = env.reset()

while not done:

# find out which actor is next to act (dictated by the env) sub_step_key, actor_id = env.actor_id()

# obtain the next action from the policy action = sample_from_policy(observation, sub_step_key, actor_id)

# step the env observation, reward, done, info = env.step(action)

# optionally use is_actor_done() to find out if the actor was terminated (relevant during training)

abstract actor_id()maze.core.env.structured_env.ActorID

Returns the current sub step key along with the currently executed actor.

The env must decide the actor in reset() and step(). In between these calls the return is constant per convention and actor_id() can be called arbitrarily.

Notes: * The id is unique only with respect to the sub step (every sub step may have its own actor 0). * Identities of done actors can not be reused in the same rollout.

Returns

The current actor, as tuple (sub step key, actor number).

abstract property agent_counts_dict

Returns the maximum count of agents per sub-step that the environment features.

If the agent count for a particular sub-step is dynamic (unknown upfront), then returns -1 for this sub-step. This then limits available training configurations (e.g. with a dynamic number of agents, only a shared policy can be trained – not multiple separate per-agent policies, as their count is not known upfront).

For example:
• For a vehicle-routing environment where max 5 driver agents will get to act during sub-step 0, this method should return {0: 5}

• For a vehicle routing environment where a dynamic number of agents will get to act during sub-step 0, this method should return {0: -1}

• For a two-step cutting environment where a piece is selected during sub-step 0 and then cut during sub-step 1 (with just one selection and cut happening in each step), this method should return {0: 1, 1: 1}

get_actor_rewards() → Optional[numpy.ndarray]

Optional. If this is a multi-step or multi-agent environment, this method should return the last reward for all actors from the last structured step.

I.e., the length of the returned array should be equal to the number of sub-steps in the last structured step.

This is useful e.g. for multi-agent cases, where we first collect actions from all agents, and then process them together and calculate rewards for all agents. In such scenario, the reward of individual agents is not know until after actions of all agents have been collected. Then, individual sub-steps might return None as agent reward, and then the reward for all agents can be queried and redistributed once the structured step is done.

Returns

If available: Array of rewards, one per each actor in the last structured step. None otherwise.

abstract is_actor_done()bool

Returns True if the just stepped actor is done, which is different to the done flag of the environment.

Like for actor_id(), the env updates this flag in reset() and step().

Returns

True if the actor is done.