  Interactive reinforcement learning agents use human feedback or instruction
to help them learn in complex environments. Often, this feedback comes in the
form of a discrete signal that is either positive or negative. While
informative, this information can be difficult to generalize on its own. In
this work, we explore how natural language advice can be used to provide a
richer feedback signal to a reinforcement learning agent by extending policy
shaping, a well-known Interactive reinforcement learning technique. Usually
policy shaping employs a human feedback policy to help an agent to learn more
about how to achieve its goal. In our case, we replace this human feedback
policy with policy generated based on natural language advice. We aim to
inspect if the generated natural language reasoning provides support to a deep
reinforcement learning agent to decide its actions successfully in any given
environment. So, we design our model with three networks: first one is the
experience driven, next is the advice generator and third one is the advice
driven. While the experience driven reinforcement learning agent chooses its
actions being influenced by the environmental reward, the advice driven neural
network with generated feedback by the advice generator for any new state
selects its actions to assist the reinforcement learning agent to better policy
shaping.
