def __init__(self,
image_shape,
num_actions,
frame_history_len=4,
replay_buffer_size=1000000,
training_freq=4,
training_starts=5000,
training_batch_size=32,
target_update_freq=1000,
reward_decay=0.99,
exploration=LinearSchedule(5000, 0.1),
log_dir="logs/"):
"""
Double Deep Q Network
params:
image_shape: (height, width, n_values)
num_actions: how many different actions we can choose
frame_history_len: feed this number of frame data as input to the deep-q Network
replay_buffer_size: size limit of replay buffer
training_freq: train base q network once per training_freq steps
training_starts: only train q network after this number of steps
training_batch_size: batch size for training base q network with gradient descent
reward_decay: decay factor(called gamma in paper) of rewards that happen in the future
exploration: used to generate an exploration factor(see 'epsilon-greedy' in paper).
when rand(0,1) < epsilon, take random action; otherwise take greedy action.
log_dir: path to write tensorboard logs
"""
super().__init__()
self.num_actions = num_actions
self.training_freq = training_freq
self.training_starts = training_starts
self.training_batch_size = training_batch_size
self.target_update_freq = target_update_freq
self.reward_decay = reward_decay
self.exploration = exploration
# use multiple frames as input to q network
input_shape = image_shape[:-1] + (image_shape[-1] * frame_history_len,)
# used to choose action
self.base_model = q_model(input_shape, num_actions)
self.base_model.compile(optimizer=optimizers.adam(clipnorm=10, lr=1e-4, decay=1e-6, epsilon=1e-4), loss='mse')
# used to estimate q values
self.target_model = q_model(input_shape, num_actions)
self.replay_buffer = ReplayBuffer(size=replay_buffer_size, frame_history_len=frame_history_len)
# current replay buffer offset
self.replay_buffer_idx = 0
self.tensorboard_callback = TensorBoard(log_dir=log_dir)
self.latest_losses = deque(maxlen=100)
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