def test():
config_path = "config.yml"
print_config(config_path)
config = load_config(config_path)
training_config = config["training_config"]
config["model_config"]["load_model"] = True
env_id = "DoublePendulum-v0"
env = GentlyTerminating(gym.make(env_id))
n_episodes = 10
max_episode_step = 10000
print("*********************************************")
print(
"Testing the model for 10 episodes with 10000 maximum steps per episode"
)
print("*********************************************")
policy = Policy(env, config)
losses = []
all_rewards = []
avg_rewards = []
epsilons = []
for i_episode in range(n_episodes):
episode_reward = 0
state = env.reset()
state[4] /= 10
epsilon = 0
epsilons.append(epsilon)
for step in range(max_episode_step):
env.render()
time.sleep(0.01)
action = policy.act(state, epsilon)
f_action = 6 * (action - (policy.n_actions - 1) / 2) / (
(policy.n_actions - 1) / 2)
next_state, reward, done, _ = env.step(f_action)
reward = 10 * reward
next_state[4] /= 10
policy.replay_buffer.push(state, action[0], reward, next_state,
done)
state = next_state
episode_reward += reward
if done:
break
print(" episode: %s, episode reward: %s" % (i_episode, episode_reward))
all_rewards.append(episode_reward)
avg_rewards.append(np.mean(all_rewards[-3:]))
env.close()
plot_fig(n_episodes, all_rewards, avg_rewards, losses)
def train():
'''Load the configuration setttings'''
config_path = "config.yml"
print_config(config_path)
config = load_config(config_path)
training_config = config["training_config"]
seed = training_config["random_seed"]
n_episodes = training_config["n_episodes"]
max_episode_step = training_config["max_episode_step"]
n_update_target = training_config["n_update_target"]
exp_number = training_config["exp_number"]
save_model_path = training_config["save_model_path"]
render_flag = training_config["render"]
save_best = training_config["save_best"]
'''Use fixed epsilon or use a exponential function decay?'''
if training_config["use_fix_epsilon"]:
epsilon_by_frame = lambda frame_idx: training_config["fix_epsilon"]
else:
epsilon_start = training_config["epsilon_start"]
epsilon_final = training_config["epsilon_final"]
epsilon_decay = training_config["epsilon_decay"]
epsilon_by_frame = lambda frame_idx: epsilon_final + (
epsilon_start - epsilon_final) * np.exp(-1. * frame_idx /
epsilon_decay)
torch.manual_seed(seed)
np.random.seed(seed)
'''Environment initialization'''
env_id = "Qube-v0"
env = GentlyTerminating(gym.make(env_id))
'''Initialize the DQN algorithm object'''
policy = Policy(env, config)
losses = []
all_rewards = []
avg_rewards = []
epsilons = []
'''Training the q-network with n episodes'''
for i_episode in range(n_episodes):
episode_reward = 0
state = env.reset()
state[4:6] /= 20
epsilon = epsilon_by_frame(i_episode)
epsilons.append(epsilon)
for step in range(max_episode_step):
if render_flag:
env.render()
'''Choose action'''
action = policy.act(state, epsilon)
f_action = 5 * (action - (policy.n_actions - 1) / 2) / (
(policy.n_actions - 1) / 2)
next_state, reward, done, _ = env.step(f_action)
reward = 100 * (reward)
next_state[4:6] /= 20
policy.replay_buffer.push(state, action[0], reward, next_state,
done)
state = next_state
episode_reward += reward
if done:
break
if len(policy.replay_buffer) > policy.batch_size:
loss = policy.train()
losses.append(loss.item())
all_rewards.append(episode_reward)
avg_rewards.append(np.mean(all_rewards[-10:]))
if i_episode % 50 == 0:
'''Save the results figure every 50 episodes'''
save_fig(i_episode, all_rewards, avg_rewards, losses, epsilons,
exp_number)
if i_episode % n_update_target == 0:
'''Update the target network'''
policy.update_target()
policy.save_model(save_model_path)
if save_best and i_episode > 100:
ratio = 1.1
if episode_reward > ratio * np.mean(all_rewards[-10:]):
print("Save model with episode reward %s " % (episode_reward))
print("Model path: %s " % (save_model_path))
break
env.close()
for t in range(10000): # Don't infinite loop while learning
action = select_action(state)
action = action.data[0].numpy()
next_state, reward, done, _ = env.step(action)
reward_sum += reward
next_state = running_state(next_state)
mask = 1
if done:
mask = 0
memory.push(state, np.array([action]), mask, next_state, reward)
if args.render:
env.render()
if done:
break
state = next_state
num_steps += (t-1)
num_episodes += 1
reward_batch += reward_sum
reward_batch /= num_episodes
batch = memory.sample()
update_params(batch)
if i_episode % args.log_interval == 0:
print('Episode {}\tLast reward: {}\tAverage reward {:.2f}'.format(
i_episode, reward_sum, reward_batch))