Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets
Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are usually tackled separately (for instance by combining recurrent neural networks and options), we show that addressing both problems simultaneously is simpler and more efficient in many cases. …