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This work is aiming to discuss and close some of the gaps in the literature on models using options (and more generally coagents). Briefly surveying the theory behind these models, it also aims to provide a unifying point of view on the many diverse examples that fall under a same category called coagent network. Motivated by the result of [10] on parameter sharing of options, we revisit the theory of (a)synchronous Coagent Network [8] by generalizing the result to the context where parameters are shared among the function approximators of coagents. The proof is more intuitive and uses the concept of execution paths in a coagent network. Theoretically, this informs us of some necessary modifications to the algorithms found in the literature which make them more mathematically accurate. It also allows us to introduce a new simple option framework, Feedforward Option Network, which outperforms the previous option models in time to convergence and stability in the famous nonstationary Four Rooms task. In addition, a stabilization effect is observed in hierarchical models which justify the unnecessity of the target network in training such models. Finally, we publish our code which allows us to be flexible in our experiments settings.
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to identify the latent state, after which it can act optimally. This setti
We introduce the textit{epistemic neural network} (ENN) as an interface for uncertainty modeling in deep learning. All existing approaches to uncertainty modeling can be expressed as ENNs, and any ENN can be identified with a Bayesian neural network.
Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interacti
We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers th