def main():
env = gym.make('CartPole-v1')
agent = QNetwork(env.observation_space, env.action_space)
frame = 0
while frame < MAX_NUM_FRAMES:
state = env.reset()
for t in range(MAX_EPISODE_DURATION):
env.render()
action = agent.act(state)
next_state, reward, done, info = env.step(action)
if done:
# done doesn't return a negative reward...
reward *= -1
frame = frame + 1
if done or frame == MAX_NUM_FRAMES:
break
state = next_state
env.close()
def main():
env = gym.make('CartPole-v1')
replay_memory = ReplayMemory()
agent = QNetwork(env.observation_space, env.action_space)
get_training_batch = make_training_transformer(env.observation_space.shape,
agent)
frame = 0
acc_loss = 0
acc_state_value = 0
while frame < MAX_NUM_FRAMES:
state = env.reset()
for t in range(MAX_EPISODE_DURATION):
if take_random_action(frame):
action = env.action_space.sample() # pick random action
else:
action = agent.act(state)
next_state, reward, done, info = env.step(action)
if done:
# on done doesn't return a negative reward...
reward *= -1
experience = (state, action, reward, next_state, done)
replay_memory.append(experience)
frame += 1
experience_samples = replay_memory.sample(BATCH_SIZE)
state_batch, qs_batch = get_training_batch(experience_samples)
acc_state_value += np.mean(qs_batch)
loss = agent.training_step(state_batch, qs_batch)
acc_loss += loss
if frame % FRAMES_PER_SAVE == 0:
model_filename = f"ckpt-loss={loss:.4f}"
agent.save_model(model_filename)
if frame % FRAMES_PER_PRINT == 0:
print(f"Frame: {frame}")
avg_loss = acc_loss / FRAMES_PER_PRINT
avg_state_value = acc_state_value / FRAMES_PER_PRINT
print(
f"avg loss: {avg_loss:.4f}; avg value: {avg_state_value:.2f}"
)
acc_loss = 0
acc_state_value = 0
if done or frame == MAX_NUM_FRAMES:
break
state = next_state
env.close()