Tag Archives: Policy Search

Human interaction with the RL process

Celemin, C., Ruiz-del-Solar, J. & Kober, A fast hybrid reinforcement learning framework with human corrective feedback, Auton Robot (2019) 43: 1173, DOI: 10.1007/s10514-018-9786-6.

Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning and Interactive Machine Learning that benefit from both sources of information, the cost function and the human corrective feedback, for accelerating the convergence and improving the final performance of the learning process. Experiments with simulated and real systems of balancing tasks and a 3 DoF robot arm validate the advantages of the proposed learning strategies: (i) they speed up the convergence of the learning process between 3 and 30 times, saving considerable time during the agent adaptation, and (ii) they allow including non-expert feedback because they have low sensibility to erroneous human advice.

Using bad results during policy iteration, and not only good ones, to improve the learning process

A. Colomé and C. Torras, Dual REPS: A Generalization of Relative Entropy Policy Search Exploiting Bad Experiences, IEEE Transactions on Robotics, vol. 33, no. 4, pp. 978-985, DOI: 10.1109/TRO.2017.2679202.

Policy search (PS) algorithms are widely used for their simplicity and effectiveness in finding solutions for robotic problems. However, most current PS algorithms derive policies by statistically fitting the data from the best experiments only. This means that experiments yielding a poor performance are usually discarded or given too little influence on the policy update. In this paper, we propose a generalization of the relative entropy policy search (REPS) algorithm that takes bad experiences into consideration when computing a policy. The proposed approach, named dual REPS (DREPS) following the philosophical interpretation of the duality between good and bad, finds clusters of experimental data yielding a poor behavior and adds them to the optimization problem as a repulsive constraint. Thus, considering that there is a duality between good and bad data samples, both are taken into account in the stochastic search for a policy. Additionally, a cluster with the best samples may be included as an attractor to enforce faster convergence to a single optimal solution in multimodal problems. We first tested our proposed approach in a simulated reinforcement learning setting and found that DREPS considerably speeds up the learning process, especially during the early optimization steps and in cases where other approaches get trapped in between several alternative maxima. Further experiments in which a real robot had to learn a task with a multimodal reward function confirm the advantages of our proposed approach with respect to REPS.