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We present new results on learning temporally extended actions forcontinuoustasks, using the options framework (Suttonet al.[1999b], Precup[2000]). In orderto achieve this goal we work with the option-criticarchitecture (Baconet al.[2017])using a deliberation cost and train it withproximal policy optimization (Schulmanet al.[2017]) instead of vanilla policygradient. Results on Mujoco domains arepromising, but lead to interestingquestions aboutwhena given option should beused, an issue directly connected tothe use of initiation sets.

Learnings Options End-to-End for Continuous Action Tasks