Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search

• URL: http://arxiv.org/abs/2010.12974v1
• Date: Sat, 24 Oct 2020 20:19:06 GMT
• Title: Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
• Authors: Jakob J. Hollenstein, Erwan Renaudo, Matteo Saveriano, Justus Piater
• Abstract summary: We propose to integrate a kinodynamic planner in the exploration strategy and to learn a control policy in an offline fashion from generated environment interactions. We compare PPS with state-of-the-art D-RL methods in typical RL settings including underactuated systems. This generates training data that helps PPS discover better policies.
• Score: 6.088695984060244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum. Furthermore, the availability of a simulation model is not fully exploited in D-RL even in simulation-based training, which potentially decreases efficiency. To better exploit simulation models in policy search, we propose to integrate a kinodynamic planner in the exploration strategy and to learn a control policy in an offline fashion from the generated environment interactions. We call the resulting model-based reinforcement learning method PPS (Planning for Policy Search). We compare PPS with state-of-the-art D-RL methods in typical RL settings including underactuated systems. The comparison shows that PPS, guided by the kinodynamic planner, collects data from a wider region of the state space. This generates training data that helps PPS discover better policies.

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