24,300 research outputs found
EMBEDDED LEARNING ROBOT WITH FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR
Fuzzy Q-learning is extending of Q-learning algorithm that uses fuzzy inference system to enable Q-learning holding continuous action and state. This learning has been implemented in various robot learning application like obstacle avoidance and target searching. However, most of them have not been realized in embedded robot. This paper presents implementation of fuzzy Q-learning for obstacle avoidance navigation in embedded mobile robot. The experimental result demonstrates that fuzzy Q-learning enables robot to be able to learn the right policy i.e. to avoid obstacle
Q Learning Behavior on Autonomous Navigation of Physical Robot
Behavior based architecture gives robot fast and reliable action. If there are many behaviors in robot, behavior coordination is needed. Subsumption architecture is behavior coordination method that give quick and robust response. Learning mechanism improve robot’s performance in handling uncertainty. Q learning is popular reinforcement learning method that has been used in robot learning because it is simple, convergent and off
policy. In this paper, Q learning will be used as learning mechanism for obstacle avoidance behavior in autonomous robot navigation. Learning rate of Q learning affect robot’s performance in learning phase. As the result,
Q learning algorithm is successfully implemented in a physical robot with its imperfect environment
Q-learning with censored data
We develop methodology for a multistage decision problem with flexible number
of stages in which the rewards are survival times that are subject to
censoring. We present a novel Q-learning algorithm that is adjusted for
censored data and allows a flexible number of stages. We provide finite sample
bounds on the generalization error of the policy learned by the algorithm, and
show that when the optimal Q-function belongs to the approximation space, the
expected survival time for policies obtained by the algorithm converges to that
of the optimal policy. We simulate a multistage clinical trial with flexible
number of stages and apply the proposed censored-Q-learning algorithm to find
individualized treatment regimens. The methodology presented in this paper has
implications in the design of personalized medicine trials in cancer and in
other life-threatening diseases.Comment: Published in at http://dx.doi.org/10.1214/12-AOS968 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Q-learning: flexible learning about useful utilities
Dynamic treatment regimes are fast becoming an important part of medicine, with the corresponding change in emphasis from treatment of the disease to treatment of the individual patient. Because of the limited number of trials to evaluate personally tailored treatment sequences, inferring optimal treatment regimes from observational data has increased importance. Q-learning is a popular method for estimating the optimal treatment regime, originally in randomized trials but more recently also in observational data. Previous applications of Q-learning have largely been restricted to continuous utility end-points with linear relationships. This paper is the first attempt at both extending the framework to discrete utilities and implementing the modelling of covariates from linear to more flexible modelling using the generalized additive model (GAM) framework. Simulated data results show that the GAM adapted Q-learning typically outperforms Q-learning with linear models and other frequently-used methods based on propensity scores in terms of coverage and bias/MSE. This represents a promising step toward a more fully general Q-learning approach to estimating optimal dynamic treatment regimes
Deep Q-learning from Demonstrations
Deep reinforcement learning (RL) has achieved several high profile successes
in difficult decision-making problems. However, these algorithms typically
require a huge amount of data before they reach reasonable performance. In
fact, their performance during learning can be extremely poor. This may be
acceptable for a simulator, but it severely limits the applicability of deep RL
to many real-world tasks, where the agent must learn in the real environment.
In this paper we study a setting where the agent may access data from previous
control of the system. We present an algorithm, Deep Q-learning from
Demonstrations (DQfD), that leverages small sets of demonstration data to
massively accelerate the learning process even from relatively small amounts of
demonstration data and is able to automatically assess the necessary ratio of
demonstration data while learning thanks to a prioritized replay mechanism.
DQfD works by combining temporal difference updates with supervised
classification of the demonstrator's actions. We show that DQfD has better
initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN)
as it starts with better scores on the first million steps on 41 of 42 games
and on average it takes PDD DQN 83 million steps to catch up to DQfD's
performance. DQfD learns to out-perform the best demonstration given in 14 of
42 games. In addition, DQfD leverages human demonstrations to achieve
state-of-the-art results for 11 games. Finally, we show that DQfD performs
better than three related algorithms for incorporating demonstration data into
DQN.Comment: Published at AAAI 2018. Previously on arxiv as "Learning from
Demonstrations for Real World Reinforcement Learning
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