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Along with the learning convergence and nonstationary equilibria, important thing to be solved in Reinforcement Learning is the slow-learning problem. In highly dynamic and stochastic systems, Markov Decision Processes is often used to model the situation, and RL is used to produce optimum control values which are expressed by optimum policy. However, approaches to solving MDP’s using RL depend on...
Robots controlled by Reinforcement Learning (RL) are still rare. A core challenge to the application of RL to robotic systems is to learn despite the existence of control delay - the delay between measuring a system's state and acting upon it. Control delay is always present in real systems. In this work, we present two novel temporal difference (TD) learning algorithms for problems with control delay...
This paper proposes a field application of a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in a cable tracking task. The underwater vehicle ICTINEUAUV learns to perform a visual based cable tracking task in a two step learning process. First, a policy is computed by means of simulation where a hydrodynamic model of the vehicle...
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