Eligibility Traces for Options

Temporally extended actions not only represent knowledge in the hierarchical setup in reinforcement learning, they also improve exploration while reducing the complexity of choosing actions. The option framework provides a concrete way to implement and reason about temporal abstraction. This work attempts to test the utility of eligibility traces with options and find good ways of doing multi-step intra-option updates. Three algorithms, based on offpolicy methods-importance sampling, tree-backup and retrace, are proposed for using eligibility traces with options.