Learning Multi-Level Hierarchies with Hindsight (1712.00948v5)

Abstract: Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously. Yet, learning multiple levels of policies in parallel is hard because it is inherently unstable: changes in a policy at one level of the hierarchy may cause changes in the transition and reward functions at higher levels in the hierarchy, making it difficult to jointly learn multiple levels of policies. In this paper, we introduce a new Hierarchical Reinforcement Learning (HRL) framework, Hierarchical Actor-Critic (HAC), that can overcome the instability issues that arise when agents try to jointly learn multiple levels of policies. The main idea behind HAC is to train each level of the hierarchy independently of the lower levels by training each level as if the lower level policies are already optimal. We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly accelerate learning relative to other non-hierarchical and hierarchical methods. Indeed, our framework is the first to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.

• The paper introduces a Hierarchical Actor-Critic (HAC) framework that trains multi-level policies in parallel.
• It leverages hindsight transitions for both actions and goals to effectively overcome sparse rewards and non-stationary dynamics.
• Empirical tests show HAC accelerates learning in grid-based and continuous robotic environments compared to prior HRL methods.

An Analysis of "Learning Multi-Level Hierarchies with Hindsight"

In their paper, "Learning Multi-Level Hierarchies with Hindsight," Levy, Konidaris, Platt, and Saenko discuss novel methodologies aimed at improving the efficiency of Hierarchical Reinforcement Learning (HRL). Specifically, they introduce the Hierarchical Actor-Critic (HAC) framework, which demonstrates the capability to learn multi-level hierarchical policies in parallel—addressing a fundamental limitation of existing HRL methods that typically require incremental, bottom-up policy training.

Core Contributions

The authors propose a framework that consists of two primary innovations: a structured hierarchical architecture and a learning mechanism to train these multi-level policies simultaneously. Their approach delineates a methodology for decomposing tasks into a series of nested, goal-conditioned policies, which enables an agent to tackle short sequences of subtasks. This multi-level policy architecture is designed to capitalize on the inherent short-horizon nature of subtasks, thereby enhancing learning efficiency, particularly in sparse reward environments.

An integral component of their method is the utilization of hindsight transitions: (i) hindsight action transitions that simulate transitions under the assumption of optimal lower-level policies; and (ii) hindsight goal transitions that help agents learn effectively using only sparse rewards. This dual transition approach allows for robust learning by addressing non-stationary transition functions that typically arise from shifting policies in HRL processes.

Experimental Validation

The framework was evaluated across various task environments, including grid-based spaces and simulated continuous domains like robotics environments. Empirically, the proposed framework demonstrated superior learning acceleration when juxtaposed with both flat RL agents and hierarchical agents from existing frameworks such as HIRO. Notably, HAC showed an unprecedented ability to learn 3-level hierarchical policies within environments characterized by continuous state and action spaces.

Implications and Future Directions

The introduction of HAC contributes theoretical advancements to HRL by providing a scalable solution to the challenge of synchronously training multi-level policies. This capability of decomposing complex decision-making into manageable, parallelizable components potentially revolutionizes applications in fields requiring adaptive and efficient learning strategies, such as robotics and autonomous systems.

Future developments may explore extending this framework to even deeper hierarchical structures and investigating the effects of varying the hierarchy's depth dynamically based on task complexity. Furthermore, the application of hindsight transitions might be explored to facilitate transfer learning across different, yet structurally similar, task environments—promoting scalability and generalizability in real-world applications.

In summary, the paper represents a significant step forward in HRL, offering a flexible and powerful hierarchical structure leveraging hindsight learning strategies to overcome previous limitations in policy training efficacy.