Showing items 1 - 4 of 4

An evaluation methodology for interactive reinforcement learning with simulated users

- Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron

  • Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
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  • Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Levels of explainable artificial intelligence for human-aligned conversational explanations

- Dazeley, Richard, Vamplew, Peter, Foale, Cameron, Young, Cameron, Aryal, Sunil, Cruz, Francisco

  • Authors: Dazeley, Richard , Vamplew, Peter , Foale, Cameron , Young, Cameron , Aryal, Sunil , Cruz, Francisco
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Artificial Intelligence Vol. 299, no. (2021), p.
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  • Description: Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for this growth include recent legislative changes and increased investments by industry and governments, along with increased concern from the general public. People are affected by autonomous decisions every day and the public need to understand the decision-making process to accept the outcomes. However, the vast majority of the applications of XAI/IML are focused on providing low-level ‘narrow’ explanations of how an individual decision was reached based on a particular datum. While important, these explanations rarely provide insights into an agent's: beliefs and motivations; hypotheses of other (human, animal or AI) agents' intentions; interpretation of external cultural expectations; or, processes used to generate its own explanation. Yet all of these factors, we propose, are essential to providing the explanatory depth that people require to accept and trust the AI's decision-making. This paper aims to define levels of explanation and describe how they can be integrated to create a human-aligned conversational explanation system. In so doing, this paper will survey current approaches and discuss the integration of different technologies to achieve these levels with Broad eXplainable Artificial Intelligence (Broad-XAI), and thereby move towards high-level ‘strong’ explanations. © 2021 Elsevier B.V.

Memory-Based explainable reinforcement learning

- Cruz, Francisco, Dazeley, Richard, Vamplew, Peter

  • Authors: Cruz, Francisco , Dazeley, Richard , Vamplew, Peter
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 32nd Australasian Joint Conference on Artificial Intelligence AI , Switzerland, 2019 in AI 2019 ; Advances in artificial intelligence ; published in Lecture Notes in Computer Science, vol 11919 , pp.66-77
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  • Description: Reinforcement learning (RL) is a learning approach based on behavioral psychology used by artificial agents to learn autonomously by interacting with their environment. An open issue in RL is the lack of visibility and understanding for end-users in terms of decisions taken by an agent during the learning process. One way to overcome this issue is to endow the agent with the ability to explain in simple terms why a particular action is taken in a particular situation. In this work, we propose a memory-based explainable reinforcement learning (MXRL) approach. Using an episodic memory, the RL agent is able to explain its decisions by using the probability of success and the number of transactions to reach the goal state. We have performed experiments considering two variations of a simulated scenario, namely, an unbounded grid world with aversive regions and a bounded grid world. The obtained results show that the agent, using information extracted from the memory, is able to explain its behavior in an understandable manner for non-expert end-users at any moment during its operation.

Memory-Based explainable reinforcement learning

- Cruz, Francisco, Dazeley, Richard, Vamplew, Peter

  • Authors: Cruz, Francisco , Dazeley, Richard , Vamplew, Peter
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 32nd Australasian Joint Conference on Artificial Intelligence AI , Switzerland, 2019 in AI 2019 ; Advances in artificial intelligence ; published in Lecture Notes in Computer Science, vol 11919 , pp.66-77
  • Full Text: false
  • Reviewed:
  • Description: Reinforcement learning (RL) is a learning approach based on behavioral psychology used by artificial agents to learn autonomously by interacting with their environment. An open issue in RL is the lack of visibility and understanding for end-users in terms of decisions taken by an agent during the learning process. One way to overcome this issue is to endow the agent with the ability to explain in simple terms why a particular action is taken in a particular situation. In this work, we propose a memory-based explainable reinforcement learning (MXRL) approach. Using an episodic memory, the RL agent is able to explain its decisions by using the probability of success and the number of transactions to reach the goal state. We have performed experiments considering two variations of a simulated scenario, namely, an unbounded grid world with aversive regions and a bounded grid world. The obtained results show that the agent, using information extracted from the memory, is able to explain its behavior in an understandable manner for non-expert end-users at any moment during its operation.
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