ray-project--ray
wehub-resource-sync
55 行
2.4 KiB
ReStructuredText
55 行
2.4 KiB
ReStructuredText
.. _rllib-hierarchical-environments-doc:
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Hierarchical Environments
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=========================
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.. include:: /_includes/rllib/new_api_stack.rst
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You can implement hierarchical training as a special case of multi-agent RL. For example, consider a two-level hierarchy of policies,
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where a top-level policy issues high level tasks that are executed at a finer timescale by one or more low-level policies.
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The following timeline shows one step of the top-level policy, which corresponds to four low-level actions:
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.. code-block:: text
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top-level: action_0 -------------------------------------> action_1 ->
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low-level: action_0 -> action_1 -> action_2 -> action_3 -> action_4 ->
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Alternatively, you could implement an environment, in which the two agent types don't act at the same time (overlappingly),
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but the low-level agents wait for the high-level agent to issue an action, then act n times before handing
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back control to the high-level agent:
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.. code-block:: text
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top-level: action_0 -----------------------------------> action_1 ->
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low-level: ---------> action_0 -> action_1 -> action_2 ------------>
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You can implement any of these hierarchical action patterns as a multi-agent environment with various
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types of agents, for example a high-level agent and a low-level agent. When set up using the correct
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agent to module mapping functions, from RLlib's perspective, the problem becomes a simple independent
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multi-agent problem with different types of policies.
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Your configuration might look something like the following:
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.. testcode::
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from ray.rllib.algorithms.ppo import PPOConfig
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config = (
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PPOConfig()
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.multi_agent(
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policies={"top_level", "low_level"},
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policy_mapping_fn=(
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lambda aid, eps, **kw: "low_level" if aid.startswith("low_level") else "top_level"
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),
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policies_to_train=["top_level"],
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)
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)
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In this setup, the appropriate rewards at any hierarchy level should be provided by the multi-agent env implementation.
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The environment class is also responsible for routing between agents, for example conveying `goals <https://arxiv.org/pdf/1703.01161.pdf>`__ from higher-level
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agents to lower-level agents as part of the lower-level agent observation.
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See `this runnable example of a hierarchical env <https://github.com/ray-project/ray/blob/master/rllib/examples/hierarchical/hierarchical_training.py>`__.
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