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We propose a reinforcement learning system designed to learn multiple different continuous state-action-space tasks. The system has been tested on a family of space-searching task akin to Morris water maze, but with obstacles. While exploring a task, the agent builds its internal model of the environment and approximates a state value function. For learning multiple tasks, we use a parametric bias switching mechanism in which the value of the parametric bias layer identifies the task for thedoi:10.1109/ijcnn.2009.5178868 dblp:conf/ijcnn/RybickiST09 fatcat:hh456v66tzbk3j2fgj2nx2t3vy