def ppo(env, brain_name, policy, config, train):
if train:
optimizier = optim.Adam(
policy.parameters(),
config['hyperparameters']['adam_learning_rate'],
eps=config['hyperparameters']['adam_epsilon'])
agent = PPOAgent(env, brain_name, policy, optimizier, config)
all_scores = []
averages = []
last_max = 30.0
for i in tqdm.tqdm(range(config['hyperparameters']['episode_count'])):
agent.step()
last_mean_reward = play_round(env, brain_name, policy, config)
if i == 0:
last_average = last_mean_reward
else:
last_average = np.mean(np.array(
all_scores[-100:])) if len(all_scores) > 100 else np.mean(
np.array(all_scores))
all_scores.append(last_mean_reward)
averages.append(last_average)
if last_average > last_max:
torch.save(
policy.state_dict(),
f"reacher-ppo/models/ppo-max-hiddensize-{config['hyperparameters']['hidden_size']}.pth"
)
last_max = last_average
clear_output(True)
print(
'Episode: {} Total score this episode: {} Last {} average: {}'.
format(i + 1, last_mean_reward, min(i + 1, 100), last_average))
return all_scores, averages
else:
all_scores = []
for i in range(20):
score = play_round(env, brain_name, policy, config, train)
all_scores.append(score)
return [score], [np.mean(score)]
n_hidden=args.n_hidden,
n_outs=n_outs,
td_n=args.td_n,
ppo_epochs=args.ppo_epochs,
mini_batch_size=args.mini_batch_size)
if args.load_best_pretrained_model:
agent.load_model('../models/ppo/model.pt')
print('Loaded pretrained model')
if args.test_env:
state = env.reset()
done = False
score = 0
while not done:
env.render()
dist, value = agent.step(state)
action = dist.sample()
state, reward, done, _ = env.step(action.cpu().numpy())
score += reward
print(score)
else:
scores = []
state = envs.reset()
next_state = None
early_stop = False
best_avg_score = args.best_avg_reward
idx = 0
while idx < args.max_frames and not early_stop:
def experiment(hidden_size=64,
lr=3e-4,
num_steps=2048,
mini_batch_size=32,
ppo_epochs=10,
threshold_reward=10,
max_episodes=15,
nrmlz_adv=True,
gamma=0.99,
tau=0.95,
clip_gradients=True):
'''
:param hidden_size: number of neurons for the layers of the model
:param lr: learning rate
:param num_steps: maximum duration of one epoch
:param mini_batch_size: mini batch size for ppo
:param ppo_epochs: number of epochs for ppo to learn
:param threshold_reward: what is the goal of the training
:param max_episodes: maximum duration of the training
:param nrmlz_adv: True, if advantages should be normalized before PPO
:param clip_gradients: True if gradients should ne clipped after PPO
:return: list of scores and list of test_rewards
'''
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
scores_window = deque(maxlen=100)
test_rewards = []
moving_averages = []
env = UnityEnvironment(file_name='reacher20/reacher', base_port=64739)
# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
# reset the environment
env_info = env.reset(train_mode=True)[brain_name]
action_size = brain.vector_action_space_size
num_agents = len(env_info.agents)
states = env_info.vector_observations
state_size = states.shape[1]
agent = PPOAgent(learning_rate=lr,
state_size=state_size,
action_size=action_size,
hidden_size=hidden_size,
num_agents=num_agents,
random_seed=0,
ppo_epochs=ppo_epochs,
mini_batch_size=mini_batch_size,
normalize_advantages=nrmlz_adv,
clip_gradients=clip_gradients,
gamma=gamma,
tau=tau,
device=device)
# while episode < max_episodes and not early_stop:
for episode in tqdm(range(max_episodes)):
log_probs = []
values = []
states_list = []
actions_list = []
rewards = []
masks = []
env_info = env.reset(train_mode=True)[brain_name]
state = env_info.vector_observations
for duration in range(num_steps):
state = torch.FloatTensor(state).to(device)
action, value, log_prob = agent.act(state)
env_info = env.step(action.cpu().data.numpy())[
brain_name] # send all actions to the environment
next_state = env_info.vector_observations # get next state (for each agent)
reward = env_info.rewards # get reward (for each agent)
dones = np.array(env_info.local_done) # see if episode finished
if reward == None:
pass
log_probs.append(log_prob)
values.append(value)
reward_t = torch.FloatTensor(reward).unsqueeze(1).to(device)
masks_t = torch.FloatTensor(1 - dones)
rewards.append(reward_t)
masks.append(masks_t)
states_list.append(state)
actions_list.append(action)
state = next_state
if np.any(dones):
break
next_state = torch.FloatTensor(state).to(device)
_, next_value, _ = agent.act(next_state)
agent.step(states=states_list,
actions=actions_list,
values=values,
log_probs=log_probs,
rewards=rewards,
masks=masks,
next_value=next_value)
test_mean_reward = test_agent(env, brain_name, agent, device)
test_rewards.append(test_mean_reward)
scores_window.append(test_mean_reward)
moving_averages.append(np.mean(scores_window))
print('Episode {}, Total score this episode: {}, Last {} average: {}'.
format(episode, test_mean_reward, min(episode, 100),
np.mean(scores_window)))
if np.mean(scores_window) > threshold_reward:
agent.save_model(
f"ppo_checkpoint_{test_mean_reward}_e{episode}_hs{hidden_size}_lr{lr}_st{num_steps}_b{mini_batch_size}_ppo{ppo_epochs}_r{threshold_reward}_e{episode}_adv{nrmlz_adv}_{test_mean_reward}.pth"
)
print(
'\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'
.format(episode, test_mean_reward))
break
episode += 1
env.close()
return scores_window, test_rewards, moving_averages
def ppo():
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = UnityEnvironment(file_name="../Reacher_Linux/Reacher.x86_64",
no_graphics=True)
# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
# reset the environment
env_info = env.reset(train_mode=True)[brain_name]
# number of agents in the environment
print('Number of agents:', len(env_info.agents))
# number of actions
action_size = brain.vector_action_space_size
print('Number of actions:', action_size)
# examine the state space
state = env_info.vector_observations[0]
print('States look like:', state)
state_size = len(state)
print('States have length:', state_size)
config = Config()
config.env = env
config.actor_critic_fn = lambda: ActorCritic(
actor=Actor(state_size, action_size), critic=Critic(state_size))
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.95
config.gradient_clip = 5
config.rollout_length = 2048
config.optimization_epochs = 5
config.num_mini_batches = 512
config.ppo_ratio_clip = 0.2
config.log_interval = 10 * 2048
config.max_steps = 2e7
config.eval_episodes = 10
# config.logger = get_logger()
print("GPU available: {}".format(torch.cuda.is_available()))
print("GPU tensor test: {}".format(torch.rand(3, 3).cuda()))
agent = PPOAgent(config)
random_seed()
config = agent.config
t0 = time.time()
scores = []
scores_window = deque(maxlen=100) # last 100 scores
while True:
if config.log_interval and not agent.total_steps % config.log_interval and len(
agent.episode_rewards):
rewards = agent.episode_rewards
for reward in rewards:
scores.append(reward)
scores_window.append(reward)
agent.episode_rewards = []
print('\r===> Average Score: {:d} episodes {:.2f}'.format(
len(scores), np.mean(scores_window)))
if np.mean(scores_window) >= 1.0:
print(
'\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'
.format(len(scores_window), np.mean(scores_window)))
torch.save(agent.actor_critic.state_dict(),
'../checkpoints/ppo_checkpoint.pth')
break
print(
'Total steps %d, returns %d/%.2f/%.2f/%.2f/%.2f (count/mean/median/min/max), %.2f steps/s'
% (agent.total_steps, len(rewards), np.mean(rewards),
np.median(rewards), np.min(rewards), np.max(rewards),
config.log_interval / (time.time() - t0)))
t0 = time.time()
agent.step()
return scores
