This thesis explores the perceived enjoyability of Deep Reinforcement learning AI agents (DeepRL agent) that strives towards optimality within the First Person Shooter game Unreal Tournament 2004 (UT2004). The DeepRL agent used in the experiments was created and then trained within this game against the AI agent which comes with the UT2004 game (known here as a trivial UT2004 agent). Through testing the opinions of participants who have played UT2004 deathmatches against both the DeepRL agent and the trivial UT2004 agent, the data collected in two participant surveys shows that the DeepRL agent is more enjoyable to face than a trivial UT2004 agent. By striving towards optimality the DeepRL agent developed a behaviour which despite making the DeepRL agent a great deal worse at UT2004 than the trivial UT2004 agent was more enjoyable to face than the trivial UT2004 agent. Considering this outcome the data suggests that DeepRL agents in UT2004 which are encouraged to strive towards optimality during training are “enjoyable enough” in order to be considered by game developers to be “good enough” when developing non-trivial opponents for games similar to UT2004. If the development time of a DeepRL agent is reduced or equal in comparison with the development time of a trivial agent then the DeepRL agent could hypothetically be preferable.