17,064 research outputs found
Output-feedback online optimal control for a class of nonlinear systems
In this paper an output-feedback model-based reinforcement learning (MBRL)
method for a class of second-order nonlinear systems is developed. The control
technique uses exact model knowledge and integrates a dynamic state estimator
within the model-based reinforcement learning framework to achieve
output-feedback MBRL. Simulation results demonstrate the efficacy of the
developed method
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
Dialogue policy learning for task-oriented dialogue systems has enjoyed great
progress recently mostly through employing reinforcement learning methods.
However, these approaches have become very sophisticated. It is time to
re-evaluate it. Are we really making progress developing dialogue agents only
based on reinforcement learning? We demonstrate how (1)~traditional supervised
learning together with (2)~a simulator-free adversarial learning method can be
used to achieve performance comparable to state-of-the-art RL-based methods.
First, we introduce a simple dialogue action decoder to predict the appropriate
actions. Then, the traditional multi-label classification solution for dialogue
policy learning is extended by adding dense layers to improve the dialogue
agent performance. Finally, we employ the Gumbel-Softmax estimator to
alternatively train the dialogue agent and the dialogue reward model without
using reinforcement learning. Based on our extensive experimentation, we can
conclude the proposed methods can achieve more stable and higher performance
with fewer efforts, such as the domain knowledge required to design a user
simulator and the intractable parameter tuning in reinforcement learning. Our
main goal is not to beat reinforcement learning with supervised learning, but
to demonstrate the value of rethinking the role of reinforcement learning and
supervised learning in optimizing task-oriented dialogue systems.Comment: 10 page
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