Learning to Explore in Motion and Interaction Tasks Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems.

CuriosityDriven Exploration for OffPolicy Reinforcement Learning Methods * Deep reinforcement learning (DRL) has achieved remarkable results in many highdimensional continuous control tasks. However, the RL agent still explores the environment randomly, resulting in low exploration efficiency and learning performance, especially in robotic manipulation tasks with sparse rewards. To address this problem, in this paper, we introduce a simplified Intrinsic Curiosity Module (SICM) into the offpolicy RL methods to encourage the agent to pursue novel and surprising states for improving the exploration competence. This method can be combined with an arbitrary offpolicy RL algorithm. We evaluate our approach on three challenging robotic manipulation tasks provided by OpenAI Gym. In our experiments, we combined our method with Deep Deterministic Policy Gradient (DDPG) with and without Hindsight Experience Replay (HER). The empirical results show that our proposed method significantly outperforms vanilla RL algorithms both in sampleefficiency and learning performance.