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Autonomous intelligent agents often must complete non-Markovian sequential tasks, which require complex recurrent neural controllers. In order to improve the convergence of evolution and reduce the computation time, this paper proposes application of an extended evolutionary algorithm. We implemented an extended multi-population genetic algorithm (EMPGA),(More)
Temporal and sequential information is essential to any agent continually interacting with its environment. In this paper, we test whether it is possible to evolve a recurrent neural network controller to match the dynamic requirement of the task. As a benchmark, we consider a sequential navigation task where the agent has to alternately visit two rewarding(More)
In this paper, a prismatic joint biped robot trajectory planning method is proposed. The minimum consumed energy is used as a criterion for trajectory generation, by using a real number genetic algorithm as an optimization tool. The minimum torque change cost function and constant vertical position trajectories are used in order to compare the results and(More)
Robots operating in everyday life environments are often required to switch between different tasks. While learning and evolution have been effectively applied to single task performance, multiple task performance still lacks methods that have been demonstrated to be both reliable and efficient. This paper introduces a new method for multiple task(More)
In most Reinforcment Learning approches, the metaparameters such as learning rate and ”temperatur” for exploration are adjusted manually. In order to build fully autonomous learning agents, it is important to develop methods for adjusting these parameters to match the demands of the task and the environment. In this paper, we propose a new method to(More)