Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)
This work offers a novel theoretical insight for researchers in machine learning by bridging RL and SL, potentially enabling new methods, though it is incremental in nature.
The paper tackles the fundamental distinction between reinforcement learning (RL) and supervised learning (SL) by proving that policy gradient methods in RL can be reformulated as a supervised learning problem, with discounted rewards replacing true labels, and provides a new proof emphasizing the link to cross entropy and a simple experiment to demonstrate this connection.
Reinforcement learning (RL) is about sequential decision making and is traditionally opposed to supervised learning (SL) and unsupervised learning (USL). In RL, given the current state, the agent makes a decision that may influence the next state as opposed to SL (and USL) where, the next state remains the same, regardless of the decisions taken, either in batch or online learning. Although this difference is fundamental between SL and RL, there are connections that have been overlooked. In particular, we prove in this paper that gradient policy method can be cast as a supervised learning problem where true label are replaced with discounted rewards. We provide a new proof of policy gradient methods (PGM) that emphasizes the tight link with the cross entropy and supervised learning. We provide a simple experiment where we interchange label and pseudo rewards. We conclude that other relationships with SL could be made if we modify the reward functions wisely.