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Dyna-style reinforcement learning (RL) agents improve sample efficiency overmodel-free RL agents by updating the value function with simulated experiencegenerated by an environment model. However, it is often difficult to learnaccurate models of environment dynamics, and even small errors may result infailure 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 notreal states of the environment. We present the Hallucinated Value Hypothesis(HVH): updating values of real states towards values of hallucinated statesresults in misleading state-action values which adversely affect the controlpolicy. We discuss and evaluate four Dyna variants; three which update realstates toward simulated -- and therefore potentially hallucinated -- states andone which does not. The experimental results provide evidence for the HVH thussuggesting a fruitful direction toward developing Dyna algorithms robust tomodel error.

Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models