  Exploration in environments with sparse rewards is difficult for artificial
agents. Curiosity driven learning -- using feed-forward prediction errors as
intrinsic rewards -- has achieved some success in these scenarios, but fails
when faced with action-dependent noise sources. We present aleatoric mapping
agents (AMAs), a neuroscience inspired solution modeled on the cholinergic
system of the mammalian brain. AMAs aim to explicitly ascertain which dynamics
of the environment are unpredictable, regardless of whether those dynamics are
induced by the actions of the agent. This is achieved by generating separate
forward predictions for the mean and variance of future states and reducing
intrinsic rewards for those transitions with high aleatoric variance. We show
AMAs are able to effectively circumvent action-dependent stochastic traps that
immobilise conventional curiosity driven agents. The code for all experiments
presented in this paper is open sourced:
http://github.com/self-supervisor/Escaping-Stochastic-Traps-With-Aleatoric-Mapping-Agents.
