usage: skiing.py [-h] [-fp FILENAME_PREFIX] [-rp RESULTS_NAME_PREFIX]
                 [-si SAVE_INTERVAL] [-rsi RESULTS_SAVE_INTERVAL]
                 [-iic INFO_INTERVAL_CURRENT] [-iim INFO_INTERVAL_MEAN]
                 [-ti TARGET_INTERVAL] [-a AGENT] [-ah AGENT_HISTORY] [-np]
                 [-nsp] [-p PLOT_NAME] [-nr] [-d DOWNSAMPLE] [-s STEPS]
                 [-ff FIT_FREQUENCY] [-no NO_OPERATION] [-e EPISODES]
                 [-eps EPSILON] [-feps FINAL_EPSILON] [-deps DECAY]
                 [-o OBSERVE] [-rm REPLAY_MEMORY] [-b BATCH] [-g GAMMA]
                 [-opt {adam,rmsprop,sgd,adagrad,adadelta,adamax}]
                 [-lr LEARNING_RATE] [-lrd LEARNING_RATE_DECAY] [-b1 BETA1]
                 [-b2 BETA2] [-rho RHO] [-f FUZZ] [-m MOMENTUM]

Trains a DQN agent to play the Atari Skiing game.

optional arguments:
  -h, --help            show this help message and exit
  -fp FILENAME_PREFIX, --filename_prefix FILENAME_PREFIX
                        Filename prefix for the trained model to be saved
                        (default out/models/atari_skiing).
  -rp RESULTS_NAME_PREFIX, --results_name_prefix RESULTS_NAME_PREFIX
                        Filename prefix for the results history to be saved
                        (default out/results/atari_skiing).
  -si SAVE_INTERVAL, --save_interval SAVE_INTERVAL
                        The save interval for the trained model (default 100),
                        in episodes.
  -rsi RESULTS_SAVE_INTERVAL, --results_save_interval RESULTS_SAVE_INTERVAL
                        The save interval for the results history (default
                        100), in episodes.Insert a negative value to not save
                        the results history.
  -iic INFO_INTERVAL_CURRENT, --info_interval_current INFO_INTERVAL_CURRENT
                        The current scoring information interval (default 20),
                        in episodes.
  -iim INFO_INTERVAL_MEAN, --info_interval_mean INFO_INTERVAL_MEAN
                        The mean scoring information interval (default 100),
                        in episodes.
  -ti TARGET_INTERVAL, --target_interval TARGET_INTERVAL
                        The target model change interval (default 10000), in
                        steps.
  -a AGENT, --agent AGENT
                        Filepath for a trained agent to be loaded (default ).
  -ah AGENT_HISTORY, --agent_history AGENT_HISTORY
                        The agent's frame history (default 4).
  -np, --no_plot        Whether the train results should not be plot.
  -nsp, --no_save_plots
                        Whether the train results plots should not be saved.
  -p PLOT_NAME, --plot_name PLOT_NAME
                        Filename prefix for the plots to be saved (default
                        out/plots/atari_skiing).
  -nr, --no_render      Whether the environment should not be rendered.
  -d DOWNSAMPLE, --downsample DOWNSAMPLE
                        The downsampling scale to be used (default 2).
  -fs FRAME_SKIPPING, --frame_skipping FRAME_SKIPPING
                        The frames to skip per action (default 4).
  -ff FIT_FREQUENCY, --fit_frequency FIT_FREQUENCY
                        The actions to take between an agent's fit (default
                        4).
  -no NO_OPERATION, --no_operation NO_OPERATION
                        The maximum number of no operation steps at the
                        beginning of the game (default 30).
  -e EPISODES, --episodes EPISODES
                        The episodes to run the training procedure (default
                        10000).
  -eps EPSILON, --epsilon EPSILON
                        The epsilon for the e-greedy policy (default 1.0).
  -feps FINAL_EPSILON, --final_epsilon FINAL_EPSILON
                        The final epsilon for the e-greedy policy (default
                        0.1).
  -deps DECAY, --decay DECAY
                        The epsilon decay for the e-greedy policy (default
                        0.0001).
  -o OBSERVE, --observe OBSERVE
                        The total number of observing steps before the
                        training begins, thus taking random actions (default
                        10000).
  -rm REPLAY_MEMORY, --replay_memory REPLAY_MEMORY
                        The replay memory to be used for the agent (default
                        400000).
  -b BATCH, --batch BATCH
                        The batch size to be randomly sampled from the memory
                        for the training (default 32).
  -g GAMMA, --gamma GAMMA
                        The discount factor (default 0.99).
  -opt {adam,rmsprop,sgd,adagrad,adadelta,adamax}, --optimizer {adam,rmsprop,sgd,adagrad,adadelta,adamax}
                        The optimizer to be used. (default RMSProp).
  -lr LEARNING_RATE, --learning_rate LEARNING_RATE
                        The learning rate for the optimizer (default 0.00025).
  -lrd LEARNING_RATE_DECAY, --learning_rate_decay LEARNING_RATE_DECAY
                        The learning rate decay for the optimizer (default
                        1e-06).
  -b1 BETA1, --beta1 BETA1
                        The beta 1 for the optimizer (default 0.9).
  -b2 BETA2, --beta2 BETA2
                        The beta 2 for the optimizer (default 0.999).
  -rho RHO              The rho for the optimizer (default 0.95).
  -f FUZZ, --fuzz FUZZ  The fuzz factor for the rmsprop optimizer (default
                        0.01).
  -m MOMENTUM, --momentum MOMENTUM
                        The momentum for the optimizer (default 0.1).