growth of computational requirements as the problem size increases, known as the curse of dimensionality [@bertsekas1995neuro]. Deep RL (DRL) tackles the curse of dimensionality due to large state spaces by utilizing a Deep Neural Network (DNN) to approximate the value function and/or the policy. This enables the agent to generalize across states without domain-specific knowledge [@tesauro1995temporal; @mnih2015human].

Despite the great success of DRL methods, deploying them in real-world applications is still limited. One of the main challenges towards that goal is dealing with large action spaces, especially when many of the actions are redundant or irrelevant (for many states). While humans can usually detect the subset of feasible actions in a given situation from the context, RL agents may attempt irrelevant actions or actions that are inferior, thus wasting computation time. Control systems for large industrial processes like power grids [@wen2015optimal; @glavic2017reinforcement; @dalal2016hierarchical] and traffic control [@mannion2016experimental; @van2016coordinated] may have millions of possible actions that can be applied at every time step. Other domains utilize natural language to represent the actions. These action spaces are typically composed of all possible sequences of words from a fixed size dictionary resulting in considerably large action spaces. Common examples of systems that use 