model.add(Dropout(dropout))
model.add(Dense(output_states))
sgd = Adam(lr=0.003, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='mean_squared_error', optimizer=sgd)
return model
q_nn = build_network(4, 6, 1, 32, "relu", 0.0)
target_nn = build_network(4, 6, 1, 32, "relu", 0.0)
target_nn.set_weights(q_nn.get_weights())
replay1 = replay_memory_agent(4, 10000)
dqn_controller = deep_q_agent(action_value_model=q_nn,
target_model=target_nn,
states_len=4,
replay_memory=replay1)
env = gym.make("CartPole-v0")
check_save = -100
# Book keeping
avg_reward_episodes = []
# Global time step
gt = 0
epsilon = 1.0
for episodes in range(0, 8000):
# Initial State
state = env.reset()
done_1 = False
import matplotlib.pyplot as plt
np.random.seed(42)
import gym
# Make the environemnt
env = gym.make("CartPole-v0")
# Tensorflow Session
sess = tf.Session()
# Initalize the neural network
ac_function = network(4, 2, sess)
target_function = network(4, 2, sess)
# Make sure both networks start from the same weight
target_function.set_weights(ac_function)
# Replay memory
replay = replay_memory_agent(4, 10000)
# Deep Q learning agent
prof_x = deep_q_agent(ac_function, target_function, 4, replay.replay_memory,
epsi_greedy)
state = env.reset()
# book keeping
done = False
episodes = 0
reward_episode = []
reward_track = []
epsilon = 1.0
while episodes < 8000:
# Pick action
state = np.asarray(state)
state = state.reshape(1, 4)
q_values = ac_function.predict_on_batch(state)
action = epsi_greedy([0, 1], q_values, epsilon)
# implement action
state_new, reward, done, _ = env.step(action)
