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In the real world, agents often have to operate in situations with incompleteinformation, limited sensing capabilities, and inherently stochasticenvironments, making individual observations incomplete and unreliable.Moreover, in many situations it is preferable to delay a decision rather thanrun the risk of making a bad decision. In such situations it is necessary toaggregate information before taking an action; however, most state of the artreinforcement learning (RL) algorithms are biased towards taking actions\textit{at every time step}, even if the agent is not particularly confident inits chosen action. This lack of caution can lead the agent to make criticalmistakes, regardless of prior experience and acclimation to the environment.Motivated by theories of dynamic resolution of uncertainty during decisionmaking in biological brains, we propose a simple accumulator module whichaccumulates evidence in favor of each possible decision, encodes uncertainty asa dynamic competition between actions, and acts on the environment only when itis sufficiently confident in the chosen action. The agent makes no decision bydefault, and the burden of proof to make a decision falls on the policy toaccrue evidence strongly in favor of a single decision. Our results show thatthis accumulator module achieves near-optimal performance on a simple guessinggame, far outperforming deep recurrent networks using traditional, forcedaction selection policies.

Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning