Soft Options Critic

2019-05-23 · Elita Lobo, Scott Jordan

The option-critic architecture (Bacon, Harb, and Precup 2017) and several variants have successfully demonstrated the use of the options framework proposed by Sutton et al (Sutton, Precup, and Singh1999) to scale learning and planning in hierarchical tasks. Although most of these frameworks use entropy as a regularizer to improve exploration, they do not maximize entropy along with returns at every time step. (Haarnoja et al., 2018d) recently introduced an off-policy actor critic algorithm in theSoft Actor Critic paper that maximize returns while maximizing entropy in a constrained manner thus enabling learning of robust options in continuous and discrete action spaces In this paper we adopt the architecture of soft-actor critic to investigate the effect of maximizing entropy of each options and inter-option policy in options framework. We derive the soft options improvement theorem and propose a novel soft-options framework to incorporate maximization of entropy of actions and options in a constrained manner. Our experiments show that the modified options-critic framework generates robust policies which allows fast recovery when environment is subjected to perturbations and outperforms vanilla options-critic framework in most hierarchical tasks

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Multitask Soft Option Learning

2019-04-01 · Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli 외

We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational posteriors for each task, regularized by…

Transfer Learning

Diversity-Enriched Option-Critic

2020-11-04 · Anand Kamat, Doina Precup

Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The option-critic framework has been demonstrated to learn temporally extended actio…

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Disentangling Options with Hellinger Distance Regularizer

2019-04-15 · Minsung Hyun, Junyoung Choi, Nojun Kwak

In reinforcement learning (RL), temporal abstraction still remains as an important and unsolved problem. The options framework provided clues to temporal abstraction in the RL, and the option-critic architecture elegantl…

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Attention Option-Critic

2022-01-07 · ICML Workshop LifelongML 2020 7 · Raviteja Chunduru, Doina Precup

Temporal abstraction in reinforcement learning is the ability of an agent to learn and use high-level behaviors, called options. The option-critic architecture provides a gradient-based end-to-end learning method to cons…

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Learning Diverse Options via InfoMax Termination Critic

2020-10-06 · Yuji Kanagawa, Tomoyuki Kaneko

We consider the problem of autonomously learning reusable temporally extended actions, or options, in reinforcement learning. While options can speed up transfer learning by serving as reusable building blocks, learning …

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