  Parameterised actions in reinforcement learning are composed of discrete
actions with continuous action-parameters. This provides a framework for
solving complex domains that require combining high-level actions with flexible
control. The recent P-DQN algorithm extends deep Q-networks to learn over such
action spaces. However, it treats all action-parameters as a single joint input
to the Q-network, invalidating its theoretical foundations. We analyse the
issues with this approach and propose a novel method, multi-pass deep
Q-networks, or MP-DQN, to address them. We empirically demonstrate that MP-DQN
significantly outperforms P-DQN and other previous algorithms in terms of data
efficiency and converged policy performance on the Platform, Robot Soccer Goal,
and Half Field Offense domains.
