Reinforcement Learning Task Clustering (RLTC)

This work represents the first step towards a task library system in the reinforcement learning domain. Task libraries could be useful in speeding up the learning of new tasks through task transfer. Related transfer can increase learning rate and can help prevent convergence to sub-optimal policies in reinforcement learning. Unrelated transfer can be extremely detrimental to the learning rate. Thus task transfer is useful in reinforcement learning if the source task and the target task are sufficiently related. Task similarity in reinforcement learning can be determined using many different similarity metrics, and simple clustering mechanisms can be applied to determine a set of related tasks. Invariants can be determined among the set of related tasks and then used in transfer. This paper uses information gathered from a set of simple grid world tasks to show that clustering of tasks based upon a similarity metric can be helpful in determining the set of source tasks which should be utilized in transfer.