  In order perform a large variety of tasks and to achieve human-level
performance in complex real-world environments, Artificial Intelligence (AI)
Agents must be able to learn from their past experiences and gain both
knowledge and an accurate representation of their environment from raw sensory
inputs. Traditionally, AI agents have suffered from difficulties in using only
sensory inputs to obtain a good representation of their environment and then
mapping this representation to an efficient control policy. Deep reinforcement
learning algorithms have provided a solution to this issue. In this study, the
performance of different conventional and novel deep reinforcement learning
algorithms was analysed. The proposed method utilises two types of algorithms,
one trained with a variant of Q-learning (DQN) and another trained with SARSA
learning (DSN) to assess the feasibility of using direct feedback alignment, a
novel biologically plausible method for back-propagating the error. These novel
agents, alongside two similar agents trained with the conventional
backpropagation algorithm, were tested by using the OpenAI Gym toolkit on
several classic control theory problems and Atari 2600 video games. The results
of this investigation open the way into new, biologically-inspired deep
reinforcement learning algorithms, and their implementation on neuromorphic
hardware.
