Intrinsically Motivated Actor-Critic for Robot Motor Learning

The performance of deep actor-critic reinforcement learning critically depends on the chosen exploration strategy. Random, undirected exploration is impractical in real-world robot learning and limits how quickly a robot can learn useful control policies. Another issue inevitable in complex robot learning tasks is using imperfect environment models for planning actions or generating experience, which results in a compounding of model errors and leads to poor task performance. In this thesis, we propose behaviorally and neurally plausible approaches to address these challenges associated with improving deep reinforcement learning for robot control. We show how learning progress-based intrinsic motivation can provide a directed exploration behavior and enable adaptive arbitration between model-based and model-free control, improving the sample efficiency of learning pixel-level control policies. Our intrinsic motivation encourages actions that lead to data that improves the model, which we use to generate imagined experiences, inspired by human mental simulation of motor behavior.

@PhdThesis{Haf20, 
 	 author =  {Hafez, Burhan}, 
 	 title = {Intrinsically Motivated Actor-Critic for Robot Motor Learning}, 
 	 school = {University of Hamburg}, 
 	 month = {Feb}, 
 	 year = {2020} 
 }