def run_vae_forward_model(self, agent, trial):
config = self._config
trial = trial + config.shift
forward_model = agent.get_motivation_module()
vae = forward_model.get_fm_network()
step_limit = int(config.steps * 1e6)
steps = 0
states = None
if config.check('generate_states'):
states = []
if config.check('collect_stats'):
states = torch.tensor(numpy.load('./{0:s}_states.npy'.format(
self._env_name)),
dtype=torch.float32)
action_list = []
value_list = []
fm_error_list = []
reward_list = []
steps_per_episode = []
train_fm_errors = []
train_ext_rewards = []
train_int_rewards = []
train_vae_losses = []
reward_avg = RunningAverageWindow(100)
step_avg = RunningAverageWindow(100)
bar = ProgressBar(config.steps * 1e6, max_width=40)
exploration = GaussianExploration(
config.sigma, 0.01, config.steps * config.exploration_time * 1e6)
while steps < step_limit:
if config.check('collect_stats'):
actions, values, fm_errors, rewards = self.fm_activations(
self._env, agent, forward_model, states)
action_list.append(actions)
value_list.append(values)
fm_error_list.append(fm_errors)
reward_list.append(rewards)
state0 = torch.tensor(self._env.reset(),
dtype=torch.float32).unsqueeze(0)
done = False
train_ext_reward = 0
train_int_reward = 0
train_vae_loss = 0
train_steps = 0
while not done:
train_steps += 1
if config.check('generate_states'):
states.append(state0.numpy())
action0 = exploration.explore(agent.get_action(state0))
next_state, reward, done, _ = self._env.step(
action0.squeeze(0).numpy())
reward = self.transform_reward(reward)
state1 = torch.tensor(next_state,
dtype=torch.float32).unsqueeze(0)
agent.train(state0, action0, state1, reward, done)
forward_model.train(state0, action0, state1)
train_ext_reward += reward
train_int_reward += forward_model.reward(
state0, action0, state1).item()
train_vae_loss += vae.loss_function(state0, action0,
state1).item()
train_fm_error = forward_model.error(state0, action0,
state1).item()
train_fm_errors.append(train_fm_error)
state0 = state1
steps += train_steps
if steps > step_limit:
train_steps -= steps - step_limit
bar.numerator = steps
exploration.update(steps)
reward_avg.update(train_ext_reward)
step_avg.update(train_steps)
steps_per_episode.append(train_steps)
train_ext_rewards.append(train_ext_reward)
train_int_rewards.append(train_int_reward)
train_vae_losses.append(train_vae_loss)
print(
'Run {0} step {1:d} sigma {2:f} training [ext. reward {3:f} int. reward {4:f} VAE loss {5:f} steps {6:d}] avg. ext. reward {7:f} avg. steps {8:f}'
.format(trial, steps, exploration.sigma, train_ext_reward,
train_int_reward, train_vae_loss, train_steps,
reward_avg.value(), step_avg.value()))
print(bar)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name,
config.model, trial))
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards),
'ri': numpy.array(train_int_rewards),
'fme': numpy.array(train_fm_errors[:step_limit]),
'vl': numpy.array(train_vae_losses),
}
numpy.save(
'ddpg_{0}_{1}_{2:d}'.format(config.name, config.model, trial),
save_data)
if config.check('generate_states'):
self.generate_states(states)
if config.check('collect_stats'):
action_list = torch.stack(action_list)
value_list = torch.stack(value_list)
fm_error_list = torch.stack(fm_error_list)
reward_list = torch.stack(reward_list)
numpy.save(
'ddpg_{0}_{1}_{2:d}_actions'.format(config.name, config.model,
trial), action_list)
numpy.save(
'ddpg_{0}_{1}_{2:d}_values'.format(config.name, config.model,
trial), value_list)
numpy.save(
'ddpg_{0}_{1}_{2:d}_prediction_errors'.format(
config.name, config.model, trial), fm_error_list)
numpy.save(
'ddpg_{0}_{1}_{2:d}_rewards'.format(config.name, config.model,
trial), reward_list)
def run_rnd_model(self, agent, trial):
config = self._config
trial = trial + config.shift
step_limit = int(config.steps * 1e6)
steps = 0
steps_per_episode = []
train_fm_errors = []
train_ext_rewards = []
train_int_rewards = []
reward_avg = RunningAverageWindow(100)
step_avg = RunningAverageWindow(100)
bar = ProgressBar(config.steps * 1e6, max_width=40)
exploration = GaussianExploration(
config.sigma, 0.01, config.steps * config.exploration_time * 1e6)
while steps < step_limit:
state0 = torch.tensor(self._env.reset(),
dtype=torch.float32).unsqueeze(0)
done = False
train_ext_reward = 0
train_int_reward = 0
train_steps = 0
while not done:
action0 = exploration.explore(agent.get_action(state0))
next_state, reward, done, _ = self._env.step(
action0.squeeze(0).numpy())
reward = self.transform_reward(reward)
state1 = torch.tensor(next_state,
dtype=torch.float32).unsqueeze(0)
reward = torch.tensor([reward],
dtype=torch.float32).unsqueeze(0)
mask = torch.tensor([done], dtype=torch.float32).unsqueeze(0)
agent.train(state0, action0, state1, reward, mask)
train_steps += 1
train_ext_reward += reward.item()
train_int_reward += agent.motivation.reward(state0).item()
train_fm_error = agent.motivation.error(state0).item()
train_fm_errors.append(train_fm_error)
state0 = state1
steps += train_steps
if steps > step_limit:
train_steps -= steps - step_limit
bar.numerator = steps
exploration.update(steps)
reward_avg.update(train_ext_reward)
step_avg.update(train_steps)
steps_per_episode.append(train_steps)
train_ext_rewards.append(train_ext_reward)
train_int_rewards.append(train_int_reward)
print(
'Run {0:d} step {1:d} sigma {2:f} training [ext. reward {3:f} int. reward {4:f} steps {5:d}] avg. ext. reward {6:f} avg. steps {7:f}'
.format(trial, steps, exploration.sigma, train_ext_reward,
train_int_reward, train_steps, reward_avg.value(),
step_avg.value()))
print(bar)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name,
config.model, trial))
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards),
'ri': numpy.array(train_int_rewards),
'fme': numpy.array(train_fm_errors[:step_limit])
}
numpy.save(
'ddpg_{0}_{1}_{2:d}'.format(config.name, config.model, trial),
save_data)
def run_forward_inverse_model(self, agent, trial):
config = self._config
trial = trial + config.shift
step_limit = int(config.steps * 1e6)
steps = 0
states = []
steps_per_episode = []
train_fm_errors = []
train_im_errors = []
train_ext_rewards = []
train_int_rewards = []
reward_avg = RunningAverageWindow(100)
step_avg = RunningAverageWindow(100)
bar = ProgressBar(config.steps * 1e6, max_width=40)
exploration = GaussianExploration(
config.sigma, 0.01, config.steps * config.exploration_time * 1e6)
while steps < step_limit:
state0 = torch.tensor(self._env.reset(),
dtype=torch.float32).unsqueeze(0)
done = False
train_ext_reward = 0
train_int_reward = 0
train_steps = 0
while not done:
train_steps += 1
states.append(state0.squeeze(0))
action0 = exploration.explore(agent.get_action(state0))
next_state, reward, done, _ = self._env.step(
action0.squeeze(0).numpy())
reward = self.transform_reward(reward)
state1 = torch.tensor(next_state,
dtype=torch.float32).unsqueeze(0)
agent.train(state0, action0, state1, reward, done)
train_ext_reward += reward
train_int_reward += agent.motivation.reward(
state0, action0, state1).item()
train_fm_error, train_im_error = agent.motivation.error(
state0, action0, state1)
train_fm_errors.append(train_fm_error.item())
train_im_errors.append(train_im_error.item())
state0 = state1
steps += train_steps
if steps > step_limit:
train_steps -= steps - step_limit
bar.numerator = steps
exploration.update(steps)
reward_avg.update(train_ext_reward)
step_avg.update(train_steps)
steps_per_episode.append(train_steps)
train_ext_rewards.append(train_ext_reward)
train_int_rewards.append(train_int_reward)
print(
'Run {0:d} step {1:d} sigma {2:f} training [ext. reward {3:f} int. reward {4:f} steps {5:d}] avg. ext. reward {6:f} avg. steps {7:f}'
.format(trial, steps, exploration.sigma, train_ext_reward,
train_int_reward, train_steps, reward_avg.value(),
step_avg.value()))
print(bar)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name,
config.model, trial))
print('Calculating distance matrices')
states = self.generate_states(torch.stack(states[:step_limit]), 500)
state_dist = cdist(states.flatten(1), states.flatten(1), 'euclidean')
index_list = numpy.argsort(numpy.linalg.norm(state_dist, axis=1))
states = states[index_list]
state_dist = cdist(states.flatten(1), states.flatten(1), 'euclidean')
latent_states = agent.network.encoder(states).detach()
latent_dist = torch.cdist(latent_states, latent_states)
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards),
'ri': numpy.array(train_int_rewards),
'fme': numpy.array(train_fm_errors[:step_limit]),
'ime': numpy.array(train_im_errors[:step_limit]),
'sdm': state_dist,
'ldm': latent_dist.numpy()
}
numpy.save(
'ddpg_{0}_{1}_{2:d}'.format(config.name, config.model, trial),
save_data)
def run_baseline(self, agent, trial):
config = self._config
n_env = config.n_env
trial = trial + config.shift
step_counter = StepCounter(int(config.steps * 1e6))
steps_per_episode = []
train_ext_rewards = []
train_ext_reward = numpy.zeros((n_env, 1), dtype=numpy.float32)
train_steps = numpy.zeros((n_env, 1), dtype=numpy.int32)
reward_avg = RunningAverageWindow(100)
# time_avg = RunningAverageWindow(100)
s = numpy.zeros((n_env,) + self._env.observation_space.shape, dtype=numpy.float32)
for i in range(n_env):
s[i] = self._env.reset(i)
state0 = self.process_state(s)
while step_counter.running():
with torch.no_grad():
value, action0, probs0 = agent.get_action(state0)
# start = time.time()
next_state, reward, done, info = self._env.step(agent.convert_action(action0.cpu()))
# end = time.time()
# time_avg.update(end - start)
# print('Duration {0:.3f}s'.format(time_avg.value()))
train_steps += 1
train_ext_reward += reward
env_indices = numpy.nonzero(numpy.squeeze(done, axis=1))[0]
for i in env_indices:
if step_counter.steps + train_steps[i] > step_counter.limit:
train_steps[i] = step_counter.limit - step_counter.steps
step_counter.update(train_steps[i].item())
steps_per_episode.append(train_steps[i])
train_ext_rewards.append(train_ext_reward[i])
reward_avg.update(train_ext_reward[i].item())
print('Run {0:d} step {1:d} training [ext. reward {2:f} steps {3:d} avg. reward {4:f}]'.format(trial, step_counter.steps, train_ext_reward[i].item(), train_steps[i].item(), reward_avg.value()))
step_counter.print()
train_ext_reward[i] = 0
train_steps[i] = 0
next_state[i] = self._env.reset(i)
state1 = self.process_state(next_state)
reward = torch.tensor(reward, dtype=torch.float32)
done = 1 - torch.tensor(done, dtype=torch.float32)
agent.train(state0, value, action0, probs0, state1, reward, done)
state0 = self.process_state(s)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name, config.model, trial))
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards)
}
numpy.save('ppo_{0}_{1}_{2:d}'.format(config.name, config.model, trial), save_data)
def run_baseline(self, agent, trial):
config = self._config
trial = trial + config.shift
step_limit = int(config.steps * 1e6)
steps = 0
bar = ProgressBar(step_limit, max_width=80)
steps_per_episode = []
train_ext_rewards = []
reward_avg = RunningAverageWindow(100)
prob_avg = RunningAverageWindow(1000, 4)
value_avg = RunningAverageWindow(1000)
while steps < step_limit:
state0 = self.process_state(self._env.reset())
done = False
train_ext_reward = 0
train_steps = 0
while not done:
value, action0, probs0 = agent.get_action(state0)
value_avg.update(value.numpy())
prob_avg.update(probs0.numpy())
next_state, reward, done, info = self._env.step(
agent.convert_action(action0.cpu()))
if isinstance(reward, numpy.ndarray):
reward = reward[0]
reward = torch.tensor([reward],
dtype=torch.float32).unsqueeze(-1)
state1 = self.process_state(next_state)
mask = torch.tensor([1], dtype=torch.float32)
if done:
mask[0] = 0
mask = mask.unsqueeze(-1)
agent.train(state0, value, action0, probs0, state1, reward,
mask)
state0 = state1
if info is not None and 'raw_score' in info:
train_ext_reward += info['raw_score']
else:
train_ext_reward += reward.item()
# train_ext_reward += reward
train_steps += 1
if steps + train_steps > step_limit:
train_steps = step_limit - steps
steps += train_steps
bar.numerator = steps
steps_per_episode.append(train_steps)
train_ext_rewards.append(train_ext_reward)
reward_avg.update(train_ext_reward)
print(
'Run {0:d} step {1:d} training [ext. reward {2:f} steps {3:d} mean reward {4:f}] prob {5:s} value {6:f}'
.format(trial, steps, train_ext_reward, train_steps,
reward_avg.value().item(),
numpy.array2string(prob_avg.value()),
value_avg.value().item()))
print(bar)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name,
config.model, trial))
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards)
}
numpy.save(
'ppo_{0}_{1}_{2:d}'.format(config.name, config.model, trial),
save_data)
def run_dop_model(self, agent, trial):
config = self._config
trial = trial + config.shift
step_limit = int(config.steps * 1e6)
steps = 0
steps_per_episode = []
train_fm_errors = []
train_ext_rewards = []
train_int_rewards = []
train_head_index = []
bar = ProgressBar(step_limit, max_width=80)
reward_avg = RunningAverageWindow(100)
while steps < step_limit:
head_index_density = numpy.zeros(4)
state0 = self.process_state(self._env.reset())
done = False
train_ext_reward = 0
train_int_reward = 0
train_error = 0
train_steps = 0
while not done:
train_steps += 1
value, action0, probs0 = agent.get_action(state0)
agent.motivation.update_state_average(state0)
action0, head_index = action0
next_state, reward, done, info = self._env.step(
agent.convert_action(action0))
state1 = self.process_state(next_state)
reward = torch.tensor([reward],
dtype=torch.float32).unsqueeze(0)
mask = torch.tensor([1], dtype=torch.float32)
if done:
mask[0] = 0
agent.train(state0, value, action0, probs0, state1, reward,
mask)
train_ext_reward += reward.item()
train_int_reward += agent.motivation.reward(state0,
probs0).item()
train_fm_error = agent.motivation.error(state0, probs0).item()
train_error += train_fm_error
train_fm_errors.append(train_fm_error)
head_index_density[head_index.item()] += 1
state0 = state1
steps += train_steps
if steps > step_limit:
train_steps -= steps - step_limit
bar.numerator = steps
steps_per_episode.append(train_steps)
train_ext_rewards.append(train_ext_reward)
train_int_rewards.append(train_int_reward)
train_head_index.append(head_index_density)
reward_avg.update(train_ext_reward)
print(
'Run {0:d} step {1:d} training [ext. reward {2:f} error {3:f} steps {4:d} ({5:f} err/step) mean reward {6:f} density {7:s}]'
.format(trial, steps, train_ext_reward, train_error,
train_steps, train_error / train_steps,
reward_avg.value(),
numpy.array2string(head_index_density)))
print(bar)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name,
config.model, trial))
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards),
'ri': numpy.array(train_int_rewards),
'fme': numpy.array(train_fm_errors[:step_limit]),
'hid': numpy.stack(train_head_index)
}
numpy.save(
'ppo_{0}_{1}_{2:d}'.format(config.name, config.model, trial),
save_data)
def run_rnd_model(self, agent, trial):
config = self._config
trial = trial + config.shift
step_limit = int(config.steps * 1e6)
steps = 0
steps_per_episode = []
train_fm_errors = []
train_ext_rewards = []
train_int_rewards = []
bar = ProgressBar(step_limit, max_width=80)
reward_avg = RunningAverageWindow(100)
while steps < step_limit:
state0 = self.process_state(self._env.reset())
done = False
train_ext_reward = 0
train_int_reward = 0
train_steps = 0
while not done:
train_steps += 1
value, action0, probs0 = agent.get_action(state0)
agent.motivation.update_state_average(state0)
next_state, reward, done, info = self._env.step(
agent.convert_action(action0.cpu()))
state1 = self.process_state(next_state)
ext_reward = torch.tensor([reward],
dtype=torch.float32).unsqueeze(0)
int_reward = agent.motivation.reward(state0).cpu()
reward = torch.cat([ext_reward, int_reward], dim=1)
mask = torch.tensor([1], dtype=torch.float32)
if done:
mask[0] = 0
mask = mask.unsqueeze(-1)
agent.train(state0, value, action0, probs0, state1, reward,
mask)
train_ext_reward += ext_reward.item()
train_int_reward += int_reward.item()
train_fm_error = agent.motivation.error(state0).item()
train_fm_errors.append(train_fm_error)
state0 = state1
steps += train_steps
if steps > step_limit:
train_steps -= steps - step_limit
bar.numerator = steps
steps_per_episode.append(train_steps)
train_ext_rewards.append(train_ext_reward)
train_int_rewards.append(train_int_reward)
reward_avg.update(train_ext_reward)
print(
'Run {0:d} step {1:d} training [ext. reward {2:f} int. reward {3:f} steps {4:d} ({5:f}) mean reward {6:f}]'
.format(trial, steps, train_ext_reward, train_int_reward,
train_steps, train_int_reward / train_steps,
reward_avg.value()))
print(bar)
agent.save('./models/{0:s}_{1}_{2:d}'.format(self._env_name,
config.model, trial))
print('Saving data...')
save_data = {
'steps': numpy.array(steps_per_episode),
're': numpy.array(train_ext_rewards),
'ri': numpy.array(train_int_rewards),
'fme': numpy.array(train_fm_errors[:step_limit])
}
numpy.save(
'ppo_{0}_{1}_{2:d}'.format(config.name, config.model, trial),
save_data)