  Dyna-style reinforcement learning (RL) agents improve sample efficiency over
model-free RL agents by updating the value function with simulated experience
generated by an environment model. However, it is often difficult to learn
accurate models of environment dynamics, and even small errors may result in
failure of Dyna agents. In this paper, we investigate one type of model error:
hallucinated states. These are states generated by the model, but that are not
real states of the environment. We present the Hallucinated Value Hypothesis
(HVH): updating values of real states towards values of hallucinated states
results in misleading state-action values which adversely affect the control
policy. We discuss and evaluate four Dyna variants; three which update real
states toward simulated -- and therefore potentially hallucinated -- states and
one which does not. The experimental results provide evidence for the HVH thus
suggesting a fruitful direction toward developing Dyna algorithms robust to
model error.
