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Many machine learning approaches in robotics, based on reinforcement learning, inverse optimal control or direct policy learning, critically rely on robot simulators. This paper investigates a simulatorfree direct policy learning, called Preference-based Policy Learning (PPL). PPL iterates a four-step process: the robot demonstrates a candidate policy; the expert ranks this policy comparatively to other ones according to her preferences; these preferences are used to learn a policy return estimate; the robot uses the policy return estimate to build new candidate policies, and the process is iterated until the desired behavior is obtained. PPL requires a good representation of the policy search space be available, enabling one to learn accurate policy return estimates and limiting the human ranking effort needed to yield a good policy. Furthermore, this representation cannot use informed features (e.g., how far the robot is from any target) due to the simulator-free setting. As a second contribution, this paper proposes a representation based on the agnostic exploitation of the robotic log. The convergence of PPL is analytically studied and its experimental validation on two problems, involving a single robot in a maze and two interacting robots, is presented.