Hands-On Intelligent Agents with OpenAI Gym

Full Q_Learner class implementation

If we put all the method implementations together, we will get a code snippet that looks like this:

EPSILON_MIN = 0.005max_num_steps = MAX_NUM_EPISODES * STEPS_PER_EPISODEEPSILON_DECAY = 500 * EPSILON_MIN / max_num_stepsALPHA = 0.05  # Learning rateGAMMA = 0.98  # Discount factorNUM_DISCRETE_BINS = 30  # Number of bins to Discretize each observation dimclass Q_Learner(object):    def __init__(self, env):        self.obs_shape = env.observation_space.shape        self.obs_high = env.observation_space.high        self.obs_low = env.observation_space.low        self.obs_bins = NUM_DISCRETE_BINS  # Number of bins to Discretize each observation dim        self.bin_width = (self.obs_high - self.obs_low) / self.obs_bins self.action_shape = env.action_space.n ...