Safe Reinforcement Learning through Meta-learned Instincts

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


An important goal in reinforcement learning is to create agents that can quickly adapt to new goals while avoiding situations that might cause damage to themselves or their environments. One way agents learn is through exploration mechanisms, which are needed to discover new policies. However, in deep reinforcement learning, exploration is normally done by injecting noise in the action space. While performing well in many domains, this setup has the inherent risk that the noisy actions performed by the agent lead to unsafe states in the environment. Here we introduce a novel approach called Meta-Learned Instinctual Networks (MLIN) that allows agents to safely learn during their lifetime while avoiding potentially hazardous states. At the core of the approach is a plastic network trained through reinforcement learning and an evolved “instinctual” network, which does not change during the agent's lifetime but can modulate the noisy output of the plastic network. We test our idea on a simple 2D navigation task with no-go zones, in which the agent has to learn to approach new targets during deployment. MLIN outperforms standard meta-trained networks and allows agents, after an evolutionary training phase, to learn to navigate to new targets without colliding with any of the no-go zones. These results suggest that meta-learning augmented with an instinctual network is a promising new approach for RL in safety-critical domains.
Original languageEnglish
Title of host publicationALIFE 2020 : The 2020 Conference on Artificial Life
Number of pages8
PublisherMIT Press
Publication date13 Jul 2020
Publication statusPublished - 13 Jul 2020
EventALIFE 2020: The 2020 Conference on Artificial Life - online, Montreal, Canada
Duration: 13 Jul 202018 Dec 2020


ConferenceALIFE 2020
Internet address
SeriesArtificial Life Conference Proceedings


  • Reinforcement learning
  • safe reinforcement learning
  • Evolutionary algorithms
  • Life-long learning


Dive into the research topics of 'Safe Reinforcement Learning through Meta-learned Instincts'. Together they form a unique fingerprint.

Cite this