Constrained Reinforcement Learning via Self-Supervision
Despite the recent advances and success of reinforcement learning methods, trial and error-based approaches have been limited to toy settings (e.g. video/board games). In safety-critical domains (e.g. self-driving vehicles, domestic robots) autonomous systems are trained against a simulator, since mistakes in the real-world cannot be tolerated. Nonetheless, simulators are hardly capable of capturing the full complexity of the problem and hence when the trained machines fail when exposed to novel settings . Consequently, we would like those systems to be able to learn online, in the real-world, without acting catastrophically, respecting pre-defined constraints. Self-supervised (a.k.a. unsupervised) reinforcement learning studies sequential decision-making without external reward functions, driven only by intrinsically-motivated utilities. The experiments will be based be based on the recently proposed OpenAI safety-gym  and a suite of unsupervised reinforcement learning methods [3, 4, 5] will be implemented and benchmarked in this framework.
Requirements: constrained optimisation, experience with deep learning frameworks (e.g. TensorFlow), reinforcement learning.
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