text: 132,117,1403298820872675329,835146121144041472,André Biedenkapp,"TempoRL has gone deep. In our new #ICML2021 paper we extend TempoRL to work in the Deep RL case and showed improved learning speed and better guided temporal exploration. If you're interested in learning *when* to act in deep RL, check out the paper <LINK>. <LINK> This was a joint work with @RaghuSpaceRajan, @FrankRHutter and @LindauerMarius",https://arxiv.org/abs/2106.05262,"Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, where an appropriate action is selected based on an observation. In this form, it is challenging to learn when it is necessary to execute new decisions. This makes learning inefficient, especially in environments that need various degrees of fine and coarse control. To address this, we propose a proactive setting in which the agent not only selects an action in a state but also for how long to commit to that action. Our TempoRL approach introduces skip connections between states and learns a skip-policy for repeating the same action along these skips. We demonstrate the effectiveness of TempoRL on a variety of traditional and deep RL environments, showing that our approach is capable of learning successful policies up to an order of magnitude faster than vanilla Q-learning. ",TempoRL: Learning When to Act,2,"[""TempoRL has gone deep. In our new #ICML2021 paper we extend TempoRL to work in the Deep RL case and showed improved learning speed and better guided temporal exploration. If you're interested in learning *when* to act in deep RL, check out the paper <LINK>. <LINK>"", 'This was a joint work with @RaghuSpaceRajan, @FrankRHutter and @LindauerMarius']",21,06,343