  An important component of many Deep Reinforcement Learning algorithms is the
Experience Replay which serves as a storage mechanism or memory of made
experiences. These experiences are used for training and help the agent to
stably find the perfect trajectory through the problem space. The classic
Experience Replay however makes only use of the experiences it actually made,
but the stored samples bear great potential in form of knowledge about the
problem that can be extracted. We present an algorithm that creates synthetic
experiences in a nondeterministic discrete environment to assist the learner.
The Interpolated Experience Replay is evaluated on the FrozenLake environment
and we show that it can support the agent to learn faster and even better than
the classic version.
