Fuzzy-Reinforcement Learning for Robot Multi-behaviour
Genetic network programming with fuzzy reinforcement learning nodes for multi behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand
... intelligent robot multi-behaviour comprising of a variety of intelligent subsystems that are fused together into one hybrid ...integrating reinforcement learning and fuzzy logic ...
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Genetic network programming with fuzzy reinforcement learning nodes for multi behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand
... intelligent robot multi-behaviour comprising of a variety of intelligent subsystems that are fused together into one hybrid ...integrating reinforcement learning and fuzzy logic ...
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A reinforcement learning approach to multi-robot planar construction
... on-line learning of the RL algorithm requires a longer time period before the first shape is created later at t = 34000, while the pre-trained RL2 cre- ates its first formation around t = ...
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A Comparison of PSO and Reinforcement Learning for Multi-Robot Obstacle Avoidance
... use multi-robot obstacle avoidance as a benchmark to compare two different evaluative learning techniques: Particle Swarm Optimization and ...single robot case, PSO and Q-learning with ...
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DeepIG: Multi-Robot Information Gathering with Deep Reinforcement Learning
... DeepIG: Multi-Robot Information Gathering with Deep Reinforcement Learning Alberto Viseras ∗ and Ricardo Garcia † Abstract—State-of-the-art multi-robot information gathering ...
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Hierarchical Reinforcement Learning Using a Modular Fuzzy Model for Multi-Agent Problem
... modular fuzzy model(depicted as ModFuzzy) show the best performance compared with the other ...the learning speed and the precision of learning are ...Q- Learning might be reasonable in such ...
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Reinforcement learning for a vision based mobile robot
... A learning system is required which can form a mapping between state, including visual information, and actuator ...supervised learning ap- proach would require a model of ‘good behaviour’ from a ...
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Collaborative reinforcement learning of autonomic behaviour
... Collaborative Reinforcement Learning (CRL) is a bottom-up approach to tackling the complex time- varying problems of engineering autonomic behaviour for distributed systems where there is no support ...
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Reinforcement Learning of Task Plans for Real Robot Systems
... A video of the robot accomplishing the task is available in https://www.youtube.com/watch?v= -KVV2ALqGx8. Finally, to compare the results that we obtained with a naive approach to the same problem, we contrast ...
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Coding for Distributed Multi-Agent Reinforcement Learning
... distributed learning frame- work for MARL, which improves the training efficiency of policy gradient algorithms in the presence of stragglers while not degrading the ...several multi-robot problems, ...
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Multi-agent reinforcement learning for intrusion detection
... DoS and DDoS attacks pose a latent threat for the Internet infrastructure. These attacks can easily disrupt important service infrastructure such as e-mail and web ser- vices [49]. In their 2008 report, Arbor Networks ...
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Individual decision making, reinforcement learning and myopic behaviour
... the learning procedure: action selection and belief ...the multi-armed bandit (MAB) problem is employed in the context of internet advertisement delayed ...
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A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance
... supervised learning may be removed by fine-tuning of the reinforcement ...neural fuzzy system with a mixed learning ...coarse learning and a fine learning ...vised ...
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Reinforcement Learning Adaptive PID Controller for an Under-actuated Robot Arm
... A fuzzy controller for an under-actuated manipulator was developed in [36] whose member functions are optimized using genetic ...planar robot with any one of the joint being a passive joint ...
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Multi Agent Reinforcement Learning
... The reinforcement learning framework can be broken down to a decentralised model naturally by letting parts of the system act and learn ...independently. Multi agent reinforcement ...
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Reinforcement learning for human-robot shared control
... the robot arm. In this case, human will move the robot arm to these areas with equilibrium positions of his/her arm, which are illustrated by ...and robot controls are required due to different ...
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Reinforcement learning for robot navigation in constrained environments
... the learning phase both in terms of exploration-exploitation trade-off and convergence rate, algorithms parameters have been accurately tuned to figure out the cor- relation between their values and the ...
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Virtual Robot Climbing using Reinforcement Learning
... Deep Reinforcement Learning have been a tremendous boon for robotics ...Deep Reinforcement Learning to train a quadruped agent to learn climbing on a variety of slope based ...
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Robot Navigation in Cluttered Environments with Deep Reinforcement Learning
... The Robot Operating System (ROS) is a flexible robotics software framework that provides many of the tools and open source software necessary to build complex robotic ...training reinforcement ...
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Reinforcement Learning as a Decision Making Strategy for a Mobile Robot
... multiple 20k 145 165 201 165 610 554 Table 5.6: Trained on on single map vs multiple map best performance Analysis: In figure 5.6 we can see the weights for the variation trained on multiple maps. The policy is mainly ...
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