-ep average best -ep average average (episode -ep average best -ep average average (episode - you've now trained your very first deep -learning agent for game as simple as frozenlakeyour deep -learning agent required episodes to train imagine if the game were far more complex how many training samples would that require to trainas it turns outthe agent could require millions of samples the number of samples required is referred to as sample complexitya concept explored further in the next section understanding bias-variance tradeoffs generally speakingsample complexity is at odds with model complexity in machine learning model complexityone wants sufficiently complex model to solve their problem for examplea model as simple as line is not sufficiently complex to predict car' trajectory sample complexityone would like model that does not require many samples this could be because they have limited access to labeled dataan insufficient amount of computing powerlimited memoryetc say we have two modelsone simple and one extremely complex for both models to attain the same performancebias-variance tells us that