Planning-integrated Policy for Efficient Reinforcement Learning in Sparse-reward Environments

Model-free reinforcement learning algorithms can learn an optimal policy from experience without requiring prior knowledge. However, model-free agents require vast amounts of samples, particularly in sparse reward environments where most states contain zero rewards. We developed a model-based approach to tackle the high sample complexity problem in sparse reward settings with continuous actions. A trained world model is queried by a particle swarm optimization (PSO) planner and employed as the action selection mechanism, hence taking the role of the actor in an actor-critic architecture. Parameters of the PSO regulate the agent's exploration rate. We show that the planner aids the agent to discover rewards even in regions with zero value gradient. Our simple planning integrated policy architecture learns more efficiently with fewer samples than continuous model-free algorithms.

@InProceedings{WWW21, 
 	 author =  {Wulur, Christoper and Weber, Cornelius and Wermter, Stefan},  
 	 title = {Planning-integrated Policy for Efficient Reinforcement Learning in Sparse-reward Environments}, 
 	 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2021},
 	 month = {Jul},
 	 publisher = {IEEE},
 	 doi = {}, 
 }