In large domains, RL agents generally require a large number of samples to learn a good policy. The options framework proposed by Sutton, Precup and Singh provides extended actions for which a policy is already learnt, reducing the complexity of the learning task, and generally making the learning task faster. An open question in the options framework is discovering the options themselves. There has been substantial work to learn options, mainly focussed around identifying ``bottleneck'' states, either empirically, or more recently, using graph theoretic methods like betweeness or graph partitions.

We would like to test an alternative hypothesis; we memorise many actions, not necessarily bottleneck ones, and put them together; based on their necessity in solving problems these actions are either reinforced, or gradually forgotten. The actions could be of varying complexity, and it is intuitive to expect that we probably learn a great deal more simple actions than complex ones. In context of the options framework, the ``complex actions'' correspond to options.

Our proposed approach is to use randomly constructed options that create a 'short-cut' between states, forming a sort of 'small-world' in the domain. This approach can be viewed as an extension of Kleinberg's popular model in the Social Network Analysis field, and we would like to note that RL domains are very grid-like as well. The analogy is further motivated by observing that the policy followed by an agent in the MDP framework is like distributed search; we are interested in moving from our source state to the destination (goal) state using only information available locally, i.e. the value function. We leave addressing the dynamics of such random options, i.e. when options are added or removed, as future work.