Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
This work addresses the challenge of learning in interactive POMDPs for AI and robotics applications, representing an incremental improvement by adapting spectral methods from passive models to active reinforcement learning settings.
The authors tackled reinforcement learning in partially observable Markov decision processes (POMDPs) by proposing a spectral decomposition-based algorithm that learns parameters from trajectories and uses an optimization oracle for policy updates, achieving an order-optimal regret bound with efficient scaling in observation and action space dimensions.
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging since the learner interacts with the environment and possibly changes the future observations in the process. We devise a learning algorithm running through epochs, in each epoch we employ spectral techniques to learn the POMDP parameters from a trajectory generated by a fixed policy. At the end of the epoch, an optimization oracle returns the optimal memoryless planning policy which maximizes the expected reward based on the estimated POMDP model. We prove an order-optimal regret bound with respect to the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces.