  $\varepsilon$-greedy is a policy used to balance exploration and exploitation
in many reinforcement learning setting. In cases where the agent uses some
on-policy algorithm to learn optimal behaviour, it makes sense for the agent to
explore more initially and eventually exploit more as it approaches the target
behaviour. This shift from heavy exploration to heavy exploitation can be
represented as decay in the $\varepsilon$ value, where $\varepsilon$ depicts
the how much an agent is allowed to explore. This paper proposes a new approach
to this $\varepsilon$ decay where the decay is based on feedback from the
environment. This paper also compares and contrasts one such approach based on
rewards and compares it against standard exponential decay. The new approach,
in the environments tested, produces more consistent results that on average
perform better.
