Mastering DeepSeek AI : A Hands-On Guide to Implementing Cutting-Edge AI Models by Sprinter Charles

Author:Sprinter, Charles
Language: eng
Format: epub
Published: 2025-01-30T00:00:00+00:00

python

replay_memory = deque(maxlen=2000)

def store_experience(state, action, reward, next_state, done):

replay_memory.append((state, action, reward, next_state, done))

Define Hyperparameters

python

batch_size = 32

gamma = 0.95 # Discount factor

epsilon = 1.0 # Exploration rate

epsilon_min = 0.01

epsilon_decay = 0.995

target_update_freq = 5 # Update target network every 5 episodes

Define the DQN Training Step

python

def train_dqn():

if len(replay_memory) < batch_size:

return

mini_batch = random.sample(replay_memory, batch_size)

states, targets_f = [], []

for state, action, reward, next_state, done in mini_batch:

state = np.array(state).reshape(1, -1)

next_state = np.array(next_state).reshape(1, -1)

target = q_network.predict(state)[0]

if done:

target[action] = reward

else:

t = target_network.predict(next_state)[0]

target[action] = reward + gamma * np.amax(t)

states.append(state[0])

targets_f.append(target)

q_network.fit(np.array(states), np.array(targets_f), epochs=1, verbose=0)

Main Training Loop

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