DeepAI AI Chat
Log In Sign Up

Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario

by   Hugo Muñoz, et al.

Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of the system need to have more information and reliability for the actions executed by an agent. In this regard, explainable reinforcement learning seeks to provide to an agent in training with methods in order to explain its behavior in such a way that users with no experience in machine learning could understand the agent's behavior. One of these is the memory-based explainable reinforcement learning method that is used to compute probabilities of success for each state-action pair using an episodic memory. In this work, we propose to make use of the memory-based explainable reinforcement learning method in a hierarchical environment composed of sub-tasks that need to be first addressed to solve a more complex task. The end goal is to verify if it is possible to provide to the agent the ability to explain its actions in the global task as well as in the sub-tasks. The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.


page 1

page 5

page 6


Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task

Explainable reinforcement learning allows artificial agents to explain t...

A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals

Two key challenges within Reinforcement Learning involve improving (a) a...

A Framework for Understanding and Visualizing Strategies of RL Agents

Recent years have seen significant advances in explainable AI as the nee...

Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario

Robotic systems are more present in our society every day. In human-robo...

On stabilizing reinforcement learning without Lyapunov functions

Reinforcement learning remains one of the major directions of the contem...

Moody Learners – Explaining Competitive Behaviour of Reinforcement Learning Agents

Designing the decision-making processes of artificial agents that are in...