  Reinforcement Learning algorithms can learn complex behavioral patterns for
sequential decision making tasks wherein an agent interacts with an environment
and acquires feedback in the form of rewards sampled from it. Traditionally,
such algorithms make decisions, i.e., select actions to execute, at every
single time step of the agent-environment interactions. In this paper, we
propose a novel framework, Fine Grained Action Repetition (FiGAR), which
enables the agent to decide the action as well as the time scale of repeating
it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm
which maintains an explicit policy estimate by enabling temporal abstractions
in the action space. We empirically demonstrate the efficacy of our framework
by showing performance improvements on top of three policy search algorithms in
different domains: Asynchronous Advantage Actor Critic in the Atari 2600
domain, Trust Region Policy Optimization in Mujoco domain and Deep
Deterministic Policy Gradients in the TORCS car racing domain.
