  One of the central challenges faced by a reinforcement learning (RL) agent is
to effectively learn a (near-)optimal policy in environments with large state
spaces having sparse and noisy feedback signals. In real-world applications, an
expert with additional domain knowledge can help in speeding up the learning
process via \emph{shaping the environment}, i.e., making the environment more
learner-friendly. A popular paradigm in literature is \emph{potential-based
reward shaping}, where the environment's reward function is augmented with
additional local rewards using a potential function. However, the applicability
of potential-based reward shaping is limited in settings where (i) the state
space is very large, and it is challenging to compute an appropriate potential
function, (ii) the feedback signals are noisy, and even with shaped rewards the
agent could be trapped in local optima, and (iii) changing the rewards alone is
not sufficient, and effective shaping requires changing the dynamics. We
address these limitations of potential-based shaping methods and propose a
novel framework of \emph{environment shaping using state abstraction}. Our key
idea is to compress the environment's large state space with noisy signals to
an abstracted space, and to use this abstraction in creating smoother and more
effective feedback signals for the agent. We study the theoretical
underpinnings of our abstraction-based environment shaping, and show that the
agent's policy learnt in the shaped environment preserves near-optimal behavior
in the original environment.
