Deep reinforcement learning for plan execution

Research output: Contribution to conferencePaperpeer-review

29 Downloads (Pure)


There are many different methods for the deliberative control of autonomous systems in stochastic environments, each with different strengths and limitations. Reinforcement Learning can provide robust performance in unpredictable environments, but its decisions are often not predictable. In contrast, Automated Planning can provide explicable and transparent behaviour but its performance drops when the environment is uncertain. In this paper we discuss an approach to plan execution through reinforcement learning by training an agent to follow predetermined plans. The implementation of the approach leads to the complex task of defining evaluation metrics that describe the desired behaviour. We describe the implementation of this approach as a set of agents, which differ in their reward function, and were trained and evaluated in three scenarios in which plan execution can deviate and be recovered.
Original languageEnglish
Number of pages8
Publication statusPublished - 17 Jun 2022
EventIntEx Workshop on Integrated Planning, Acting, and Execution - Virtual
Duration: 17 Jun 202217 Jun 2022


ConferenceIntEx Workshop on Integrated Planning, Acting, and Execution
Abbreviated titleIntEx 2022
Internet address


  • deep reinforcement learning
  • plan execution
  • autonomous systems
  • stochastic environments
  • reinforcement learning (RL)
  • AI planning (AIP)


Dive into the research topics of 'Deep reinforcement learning for plan execution'. Together they form a unique fingerprint.

Cite this