# Graphs below give you a chance to look deeper into your car's behaviour on track. # # We start with plot_selected_laps. The general idea of this block is as follows: # * Select laps(episodes) that have the properties that you care about, for instance, fastest, most progressed, failing in a certain section of the track or not failing in there, # * Provide the list of them in a dataframe into the plot_selected_laps, together with the whole training dataframe and the track info, # * You've got the laps to analyse. # + # Some examples: # highest reward for complete laps: episodes_to_plot = complete_ones.nlargest(3, 'reward') # highest progress from all episodes: # episodes_to_plot = simulation_agg.nlargest(3,'progress') pu.plot_selected_laps(episodes_to_plot, df, track) # - # ### Plot a heatmap of rewards for current training. # The brighter the colour, the higher the reward granted in given coordinates. # If instead of a similar view as in the example below you get a dark image with hardly any # dots, it might be that your rewards are highly disproportionate and possibly sparse. # # Disproportion means you may have one reward of 10.000 and the rest in range 0.01-1. # In such cases the vast majority of dots will simply be very dark and the only bright dot # might be in a place difficult to spot. I recommend you go back to the tables and show highest # and average rewards per step to confirm if this is the case. Such disproportions may # not affect your traning very negatively, but they will make the data less readable in this notebook. # # Sparse data means that the car gets a high reward for the best behaviour and very low reward # for anything else, and worse even, reward is pretty much discrete (return 10 for narrow perfect, # else return 0.1). The car relies on reward varying between behaviours to find gradients that can
# Graphs below give you a chance to look deeper into your car's behaviour on track. # # We start with plot_selected_laps. The general idea of this block is as follows: # * Select laps(episodes) that have the properties that you care about, for instance, fastest, most progressed, failing in a certain section of the track or not failing in there, # * Provide the list of them in a dataframe into the plot_selected_laps, together with the whole training dataframe and the track info, # * You've got the laps to analyse. # + # Some examples: # highest reward for complete laps: # episodes_to_plot = complete_ones.nlargest(3,'reward') # highest progress from all episodes: episodes_to_plot = simulation_agg.nlargest(3,'progress') pu.plot_selected_laps(episodes_to_plot, df, track) # - # ### Plot a heatmap of rewards for current training. # The brighter the colour, the higher the reward granted in given coordinates. # If instead of a similar view as in the example below you get a dark image with hardly any # dots, it might be that your rewards are highly disproportionate and possibly sparse. # # Disproportion means you may have one reward of 10.000 and the rest in range 0.01-1. # In such cases the vast majority of dots will simply be very dark and the only bright dot # might be in a place difficult to spot. I recommend you go back to the tables and show highest # and average rewards per step to confirm if this is the case. Such disproportions may # not affect your traning very negatively, but they will make the data less readable in this notebook. # # Sparse data means that the car gets a high reward for the best behaviour and very low reward # for anything else, and worse even, reward is pretty much discrete (return 10 for narrow perfect, # else return 0.1). The car relies on reward varying between behaviours to find gradients that can