Safety and Life-long Reinforcement Learning
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Description
Random exploration is one of the main mechanisms through which reinforcement learning (RL) solves tasks. However, random exploration can lead to undesirable or catastrophic outcomes when learning in safety-critical environments. In fact, safe learning is one of the major obstacles towards real-world agents that can learn during deployment. This project revolves around approaches that automatically train sub-modules that shape learning such that the agent will instinctively avoid dangers while still being capable of exploring its surroundings to learn new tasks.
Related Research
Grbic, D & Risi, S 2020, Safe Reinforcement Learning through Meta-learned Instincts. in ALIFE 2020: The 2020 Conference on Artificial Life. 32 edn, MIT Press, Artificial Life Conference Proceedings , pp. 183-291, ALIFE 2020, Montreal, Canada, 13/07/2020. https://doi.org/10.1162/isal_a_00318
Djordje Grbic, Sebastian Risi; July 19–23, 2021. “Safer Reinforcement Learning through Transferable Instinct Networks.” Proceedings of the ALIFE 2021: The 2021 Conference on Artificial Life. ALIFE 2021: The 2021 Conference on Artificial Life. Online. (pp. 107). ASME. https://doi.org/10.1162/isal_a_00449