Hierarchical environments#
You can implement hierarchical training as a special case of multi-agent RL. For example, consider a two-level hierarchy of policies, where a top-level policy issues high-level tasks that one or more low-level policies execute at a finer timescale. The following timeline shows one step of the top-level policy, which corresponds to four low-level actions:
top-level: action_0 -------------------------------------> action_1 ->
low-level: action_0 -> action_1 -> action_2 -> action_3 -> action_4 ->
Alternatively, you can implement an environment in which the two agent types don’t act at the same time. The low-level agents wait for the high-level agent to issue an action, then act n times before handing control back to the high-level agent:
top-level: action_0 -----------------------------------> action_1 ->
low-level: ---------> action_0 -> action_1 -> action_2 ------------>
You can implement any of these hierarchical action patterns as a multi-agent environment with various types of agents, such as a high-level agent and a low-level agent. When you set up the correct agent-to-module mapping functions, RLlib sees an independent multi-agent problem with different types of policies.
Your configuration might look like the following:
from ray.rllib.algorithms.ppo import PPOConfig
config = (
PPOConfig()
.multi_agent(
policies={"top_level", "low_level"},
policy_mapping_fn=(
lambda aid, eps, **kw: "low_level" if aid.startswith("low_level") else "top_level"
),
policies_to_train=["top_level"],
)
)
In this setup, the multi-agent environment implementation should provide the appropriate rewards at any hierarchy level. The environment class is also responsible for routing between agents. For example, it conveys goals from higher-level agents to lower-level agents as part of the lower-level agent’s observation.