DeepAI AI Chat
Log In Sign Up

Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering

This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the sequence of actions that lead to transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. These skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over the same model. This approach works well even without the exact model of the environment by using sample trajectories to construct an approximate estimate. We also present our approach to scaling the skill acquisition framework to complex tasks with large state spaces for which we perform state aggregation using the representation learned from an action conditional video prediction network and use the skill acquisition framework on the aggregated state space.


page 4

page 6


Successor Options: An Option Discovery Framework for Reinforcement Learning

The options framework in reinforcement learning models the notion of a s...

Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning

Learning policies for complex tasks that require multiple different skil...

Learning Task Agnostic Skills with Data-driven Guidance

To increase autonomy in reinforcement learning, agents need to learn use...

Abstract Value Iteration for Hierarchical Reinforcement Learning

We propose a novel hierarchical reinforcement learning framework for con...

Constructing Abstraction Hierarchies Using a Skill-Symbol Loop

We describe a framework for building abstraction hierarchies whereby an ...

Learning to Plan Hierarchically from Curriculum

We present a framework for learning to plan hierarchically in domains wi...

Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning

Option discovery and skill acquisition frameworks are integral to the fu...