Abstract
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL framework, MP3 enables the generation of smooth trajectories throughout the whole learning process while effectively learning from sparse and non-Markovian rewards. Additionally, MP3 maintains the capability to adapt to changes in the environment during execution. Although many early successes in robot RL have been achieved by combining RL with MPs, these approaches are often limited to learning single stroke-based motions, lacking the ability to adapt to task variations or adjust motions during execution. Building upon our previous work, which introduced an episode-based RL method for the non-linear adaptation of MP parameters to different task variations, this paper extends the approach to incorporating replanning strategies. This allows adaptation of the MP parameters throughout motion execution, addressing th