Contents
What is hierarchical RL?
Definition. Hierarchical reinforcement learning (HRL) decomposes a reinforcement learning problem into a hierarchy of subproblems or subtasks such that higher-level parent-tasks invoke lower-level child tasks as if they were primitive actions. A decomposition may have multiple levels of hierarchy.
What is multi task reinforcement learning?
From the perspective of reinforcement learning, multi-task learning is an approach intended to optimize the performance of an agent under the assumption that performance bottle-neck problems experienced by the agent are drawn from the same distribution.
Are there any libraries for multi agent RL?
There are currently few libraries for multi-agent RL, which increases the initial overhead of experimenting with multi-agent approaches. Our goal is to reduce this friction and make it easy to go from single-agent to multi-agent RL in both research and application.
Is it difficult to build multi-agent learning software?
Building software for a rapidly developing field such as RL is challenging, multi-agent especially so. This is in part due to the breadth of techniques used to deal with the core issues that arise in multi-agent learning. Consider one such issue: environment non-stationarity.
Where can I find multi agent reinforcement learning?
An earlier version of this post is on the RISELab blog. It is posted here with the permission of the authors. We just rolled out general support for multi-agent reinforcement learning in Ray RLlib 0.6.0. This blog post is a brief tutorial on multi-agent RL and how we designed for it in RLlib.
How are multi agent environments used in rllib?
RLlib multi-agent environments can model multiple independent agents that come and go over time. Different policies can be assigned to agents as they appear. This is formalized in the MultiAgentEnv interface, which can returns observations and rewards from multiple ready agents per step: