def __init__(self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None,
n_envs=1):
self.envs = envs
self.n_envs = n_envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
self.score = 0.0
assert self.agent.mode in [ACMode.PPO, ACMode.A2C]
self.is_ppo = self.agent.mode == ACMode.PPO
if self.is_ppo:
assert ppo_par is not None
# assert n_steps * envs.n_envs % ppo_par.batch_size == 0
# assert n_steps * envs.n_envs >= ppo_par.batch_size
assert n_steps * self.envs.num_envs % ppo_par.batch_size == 0
assert n_steps * self.envs.num_envs >= ppo_par.batch_size
self.ppo_par = ppo_par
def test_invalid_and_dim(self):
action = ([3], [[14, 3]])
with self.assertRaises(AssertionError):
ActionProcesser(dim=5, rect_delta=7).process(*action)
assert ActionProcesser(dim=15, rect_delta=2).process(*action) == \
[FunctionCall(function=3, arguments=[[0], [12, 1], [14, 5]])]
assert ActionProcesser(dim=40, rect_delta=2).process(*action) == \
[FunctionCall(function=3, arguments=[[0], [12, 1], [16, 5]])]
def __init__(
self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None,
n_envs=1,
policy_type = None
):
self.envs = envs
self.n_envs = n_envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=8)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
self.score = 0.0
# self.policy_type = FullyConvPolicy if ( (policy_type == 'FullyConv') or (policy_type == 'Relational')) else MetaPolicy
if policy_type == 'FullyConv':
self.policy_type = FullyConvPolicy
elif policy_type == 'Relational':
self.policy_type = RelationalPolicy
elif policy_type == 'MetaPolicy':
self.policy_type = MetaPolicy
elif policy_type == 'FullyConv3D':
self.policy_type = FullyConv3DPolicy
elif policy_type == 'AlloAndAlt':
self.policy_type = FullyConvPolicyAlt
elif (policy_type == 'Factored'):
self.policy_type = FactoredPolicy
elif (policy_type == 'FactoredPolicy_PhaseI'):
self.policy_type = FactoredPolicy_PhaseI
elif (policy_type == 'FactoredPolicy_PhaseII'):
self.policy_type = FactoredPolicy_PhaseII
else: print('Unknown Policy')
assert self.agent.mode in [ACMode.A2C, ACMode.PPO]
self.is_ppo = self.agent.mode == ACMode.PPO
if self.is_ppo:
assert ppo_par is not None
# assert n_steps * envs.n_envs % ppo_par.batch_size == 0
# assert n_steps * envs.n_envs >= ppo_par.batch_size
assert n_steps * self.envs.num_envs % ppo_par.batch_size == 0
assert n_steps * self.envs.num_envs >= ppo_par.batch_size
self.ppo_par = ppo_par
def __init__(
self,
envs,
agent: A2CAgent,
n_steps=5,
discount=0.99,
do_training=True
):
self.envs = envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.batch_counter = 0
self.episode_counter = 0
def __init__(self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None,
episodes=3):
self.envs = envs
self.agent = agent # assign a variable agent to the AC which comes from the module of your choice
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
self.episodes = episodes
def test_simple(self):
a = ActionProcesser(
dim=40,
rect_delta=5,
)
action_ids = [2, 331, 0, 1]
coords = ((15, 4), (22, 33), (1, 1), (1, 1))
actions = a.process(action_ids, coords)
expected = [
FunctionCall(function=2, arguments=[[0], (4, 15)]),
FunctionCall(function=331, arguments=[[0], (33, 22)]),
FunctionCall(function=0, arguments=[]),
FunctionCall(function=1, arguments=[(1, 1)])
]
assert actions == expected
def test_rectangle(self):
dim = 48
a = ActionProcesser(
dim=dim,
rect_delta=7,
)
action_ids = [3, 3, 3, 3]
coords = ((15, 4), (22, 33), (1, 1), (45, 10))
actions = a.process(action_ids, coords)
expected = [
FunctionCall(function=3, arguments=[[0], [8, 0], [22, 11]]),
FunctionCall(function=3, arguments=[[0], [15, 26], [29, 40]]),
FunctionCall(function=3, arguments=[[0], [0, 0], [8, 8]]),
FunctionCall(function=3, arguments=[[0], [38, 3], [47, 17]])
]
assert actions == expected
class Runner(object):
def __init__(self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None,
episodes=3):
self.envs = envs
self.agent = agent # assign a variable agent to the AC which comes from the module of your choice
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
self.episodes = episodes
def reset(self):
obs = self.envs.reset()
self.latest_obs = self.obs_processer.process(obs)
def _log_score_to_tb(self, score):
summary = tf.Summary()
summary.value.add(tag='sc2/episode_score', simple_value=score)
self.agent.summary_writer.add_summary(summary, self.episode_counter)
def _handle_episode_end(self, timestep):
score = timestep.observation["score_cumulative"][0]
print("episode %d ended. Score %f" % (self.episode_counter, score))
self._log_score_to_tb(score)
self.episode_counter += 1
def run_batch(self):
mb_actions = []
mb_obs = []
mb_values = np.zeros((self.envs.n_envs, self.n_steps + 1),
dtype=np.float32)
mb_rewards = np.zeros((self.envs.n_envs, self.n_steps),
dtype=np.float32)
latest_obs = self.latest_obs # state(t0)
rnn_state = self.agent.state_init
for n in range(self.n_steps):
action_ids, spatial_action_2ds, value_estimate, rnn_state = self.agent.step(
latest_obs, rnn_state)
#print('step: ', n, action_ids, spatial_action_2ds, value_estimate) # for debugging
# Store actions and value estimates for all steps (this is being done in parallel with the other envs)
mb_values[:, n] = value_estimate
mb_obs.append(latest_obs)
mb_actions.append((action_ids, spatial_action_2ds))
# Do action, return it to environment, get new obs and reward, store reward
actions_pp = self.action_processer.process(action_ids,
spatial_action_2ds)
obs_raw = self.envs.step(actions_pp)
latest_obs = self.obs_processer.process(obs_raw) # state(t+1)
mb_rewards[:, n] = [t.reward for t in obs_raw]
#Check for all convolutional lstm vizdoom if last state is true
for t in obs_raw:
if t.last():
self._handle_episode_end(t)
# Get the Vt+1 and use it as a future reward from st+1 as we dont know the actual reward (bootstraping here with net's predicitons)
mb_values[:, -1] = self.agent.get_value(latest_obs, rnn_state)
n_step_advantage = general_n_step_advantage(mb_rewards,
mb_values,
self.discount,
lambda_par=1.0)
full_input = {
# these are transposed because action/obs
# processers return [time, env, ...] shaped arrays
FEATURE_KEYS.advantage:
n_step_advantage.transpose(),
FEATURE_KEYS.value_target:
(n_step_advantage + mb_values[:, :-1]).transpose()
}
# We combine all experiences from every env
full_input.update(self.action_processer.combine_batch(mb_actions))
full_input.update(self.obs_processer.combine_batch(mb_obs))
full_input = {
k: combine_first_dimensions(v)
for k, v in full_input.items()
}
if not self.do_training:
pass
else:
self.agent.train(
full_input, rnn_state
) # You might want to reset the state between forward and backward pass
self.latest_obs = latest_obs #state(t) = state(t+1)
self.batch_counter += 1
print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
def run_trained_batch(self):
# This function enables testing from a trained model. It loads the weights and runs the model for some episodes.
latest_obs = self.latest_obs # state(t0)
rnn_state = self.agent.state_init
while self.episode_counter <= (self.episodes - 1):
action_ids, spatial_action_2ds, value_estimate, rnn_state = self.agent.step(
latest_obs,
rnn_state) # (MINE) AGENT STEP = INPUT TO NN THE CURRENT
#print('action: ', actions.FUNCTIONS[action_ids[0]].name, 'on', 'x=', spatial_action_2ds[0][0], 'y=', spatial_action_2ds[0][1], 'Value=', value_estimate[0]) # for debugging
actions_pp = self.action_processer.process(action_ids,
spatial_action_2ds)
obs_raw = self.envs.step(actions_pp)
latest_obs = self.obs_processer.process(obs_raw)
# Check for all observations if last state is true
for t in obs_raw:
if t.last():
self._handle_episode_end(t)
self.latest_obs = latest_obs # state(t) = state(t+1)
self.batch_counter += 1
#print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
class Runner(object):
def __init__(self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None):
self.envs = envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
assert self.agent.mode in [ACMode.PPO, ACMode.A2C]
self.is_ppo = self.agent.mode == ACMode.PPO
if self.is_ppo:
assert ppo_par is not None
assert n_steps * envs.n_envs % ppo_par.batch_size == 0
assert n_steps * envs.n_envs >= ppo_par.batch_size
self.ppo_par = ppo_par
def reset(self):
obs = self.envs.reset()
#print(min_distance_to_enemy(obs[0], minimap=True))
self.last_min_dist_to_enemy = min_distance_to_enemy(obs[0],
minimap=True)
#print(count_units(obs[0], minimap=False))
self.units_in_frame = count_units(obs[0], minimap=False)
self.latest_obs = self.obs_processer.process(obs)
def _log_score_to_tb(self, score):
summary = tf.Summary()
summary.value.add(tag='sc2/episode_score', simple_value=score)
self.agent.summary_writer.add_summary(summary, self.episode_counter)
def _log_modified_to_tb(self, score):
summary = tf.Summary()
summary.value.add(tag='sc2/episode_score_modified', simple_value=score)
self.agent.summary_writer.add_summary(summary, self.episode_counter)
def _handle_episode_end(self, timestep):
score = timestep.observation["score_cumulative"][0]
print("episode %d ended. Score %f" % (self.episode_counter, score))
self._log_score_to_tb(score)
self.episode_counter += 1
def _train_ppo_epoch(self, full_input):
total_obs = self.n_steps * self.envs.n_envs
shuffle_idx = np.random.permutation(total_obs)
batches = dict_of_lists_to_list_of_dicst({
k: np.split(v[shuffle_idx], total_obs // self.ppo_par.batch_size)
for k, v in full_input.items()
})
for b in batches:
self.agent.train(b)
def run_batch(self):
mb_actions = []
mb_obs = []
mb_values = np.zeros((self.envs.n_envs, self.n_steps + 1),
dtype=np.float32)
mb_rewards = np.zeros((self.envs.n_envs, self.n_steps),
dtype=np.float32)
mb_rewards_modified = np.zeros((self.envs.n_envs, self.n_steps),
dtype=np.float32)
latest_obs = self.latest_obs
for n in range(self.n_steps):
# could calculate value estimate from obs when do training
# but saving values here will make n step reward calculation a bit easier
action_ids, spatial_action_2ds, value_estimate = self.agent.step(
latest_obs)
mb_values[:, n] = value_estimate
mb_obs.append(latest_obs)
mb_actions.append((action_ids, spatial_action_2ds))
actions_pp = self.action_processer.process(action_ids,
spatial_action_2ds)
obs_raw = self.envs.step(actions_pp)
latest_obs = self.obs_processer.process(obs_raw)
mb_rewards[:, n] = [t.reward for t in obs_raw]
# NEW
i = 0
last_dist = self.last_min_dist_to_enemy
#print(last_dist)
curr_dist = min_distance_to_enemy(obs_raw[0], minimap=True)
#print(curr_dist)
if last_dist < INF and curr_dist < INF:
mb_rewards_modified[:, n] = [
t.reward + (last_dist - curr_dist) / 20 for t in obs_raw
]
self.last_min_dist_to_enemy = curr_dist
###
for t in obs_raw:
if t.last():
self._handle_episode_end(t)
mb_values[:, -1] = self.agent.get_value(latest_obs)
n_step_advantage = general_n_step_advantage(
mb_rewards,
mb_rewards_modified,
mb_values,
self.discount,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0)
full_input = {
# these are transposed because action/obs
# processers return [time, env, ...] shaped arrays
FEATURE_KEYS.advantage:
n_step_advantage.transpose(),
FEATURE_KEYS.value_target:
(n_step_advantage + mb_values[:, :-1]).transpose()
}
full_input.update(self.action_processer.combine_batch(mb_actions))
full_input.update(self.obs_processer.combine_batch(mb_obs))
full_input = {
k: combine_first_dimensions(v)
for k, v in full_input.items()
}
if not self.do_training:
pass
elif self.agent.mode == ACMode.A2C:
self.agent.train(full_input)
elif self.agent.mode == ACMode.PPO:
for epoch in range(self.ppo_par.n_epochs):
self._train_ppo_epoch(full_input)
self.agent.update_theta()
self.latest_obs = latest_obs
self.batch_counter += 1
sys.stdout.flush()
class Runner(object):
def __init__(
self,
envs,
agent: A2CAgent,
n_steps=5,
discount=0.99,
do_training=True
):
self.envs = envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.batch_counter = 0
self.episode_counter = 0
def reset(self):
obs = self.envs.reset()
self.latest_obs = self.obs_processer.process(obs)
def _log_score_to_tb(self, score):
summary = tf.Summary()
summary.value.add(tag='sc2/episode_score', simple_value=score)
self.agent.summary_writer.add_summary(summary, self.episode_counter)
def _handle_episode_end(self, timestep):
score = timestep.observation["score_cumulative"][0]
print("episode %d ended. Score %f" % (self.episode_counter, score))
self._log_score_to_tb(score)
self.episode_counter += 1
def run_batch(self):
dim = (self.envs.n_envs, self.n_steps)
mb_rewards = np.zeros(dim, dtype=np.float32)
mb_actions = []
mb_obs = []
latest_obs = self.latest_obs
for n in range(self.n_steps):
# values is not used for anything but calculated anyway
action_ids, spatial_action_2ds = self.agent.step(latest_obs)
mb_obs.append(latest_obs)
mb_actions.append((action_ids, spatial_action_2ds))
actions_pp = self.action_processer.process(action_ids, spatial_action_2ds)
obs_raw = self.envs.step(actions_pp)
latest_obs = self.obs_processer.process(obs_raw)
mb_rewards[:, n] = [t.reward for t in obs_raw]
for t in obs_raw:
if t.last():
self._handle_episode_end(t)
last_state_values = self.agent.get_value(latest_obs)
n_step_rewards = calculate_n_step_reward(mb_rewards, self.discount, last_state_values)
mb_actions_combined = self.action_processer.combine_batch(mb_actions)
mb_obs_combined = self.obs_processer.combine_batch(mb_obs)
if self.do_training:
self.agent.train(
n_step_rewards.transpose(),
mb_obs_combined=mb_obs_combined,
mb_actions_combined=mb_actions_combined
)
self.latest_obs = latest_obs
self.batch_counter += 1
sys.stdout.flush()
class Runner(object):
def __init__(self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None,
n_envs=1):
self.envs = envs
self.n_envs = n_envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=flags.FLAGS.resolution)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
self.score = 0.0
assert self.agent.mode in [ACMode.PPO, ACMode.A2C]
self.is_ppo = self.agent.mode == ACMode.PPO
if self.is_ppo:
assert ppo_par is not None
# assert n_steps * envs.n_envs % ppo_par.batch_size == 0
# assert n_steps * envs.n_envs >= ppo_par.batch_size
assert n_steps * self.envs.num_envs % ppo_par.batch_size == 0
assert n_steps * self.envs.num_envs >= ppo_par.batch_size
self.ppo_par = ppo_par
def reset(self):
#self.score = 0.0
obs = self.envs.reset()
self.latest_obs = self.obs_processer.process(obs)
def reset_demo(self):
#self.score = 0.0
obs = self.envs.reset()
self.latest_obs = self.obs_processer.process([obs])
def _log_score_to_tb(self, score):
summary = tf.Summary()
summary.value.add(tag='sc2/episode_score', simple_value=score)
self.agent.summary_writer.add_summary(summary, self.episode_counter)
def _handle_episode_end(self, timestep, length, last_step_r):
#(MINE) This timestep is actually the last set of feature observations
#score = timestep.observation["score_cumulative"][0]
#self.score = (self.score + timestep) # //self.episode_counter # It is zero at the beginning so you get inf
self.score = timestep
print(
">>>>>>>>>>>>>>>episode %d ended. Score %f | Total Steps %d | Last step Reward %f"
% (self.episode_counter, self.score, length, last_step_r))
self._log_score_to_tb(self.score) # logging score to tensorboard
self.episode_counter += 1 # Is not used for stopping purposes judt for printing. You train for a number of batches (nsteps+training no matter reset)
#self.reset() # Error if Monitor doesnt have the option to reset without an env to be done (THIS RESETS ALL ENVS!!! YOU NEED remot.send(env.reset) to reset a specific env. Else restart within the env
def _train_ppo_epoch(self, full_input):
total_obs = self.n_steps * self.envs.num_envs
shuffle_idx = np.random.permutation(total_obs)
batches = dict_of_lists_to_list_of_dicst({
k: np.split(v[shuffle_idx], total_obs // self.ppo_par.batch_size)
for k, v in full_input.items()
})
for b in batches:
self.agent.train(b)
def run_batch(self):
#(MINE) MAIN LOOP!!!
mb_actions = []
mb_obs = []
mb_values = np.zeros((self.envs.num_envs, self.n_steps + 1),
dtype=np.float32)
mb_rewards = np.zeros((self.envs.num_envs, self.n_steps),
dtype=np.float32)
mb_done = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.int32)
latest_obs = self.latest_obs # (MINE) =state(t)
for n in range(self.n_steps):
# could calculate value estimate from obs when do training
# but saving values here will make n step reward calculation a bit easier
action_ids, value_estimate = self.agent.step(latest_obs)
print(
'|step:', n, '|actions:', action_ids
) # (MINE) If you put it after the envs.step the SUCCESS appears at the envs.step so it will appear oddly
# (MINE) Store actions and value estimates for all steps
mb_values[:, n] = value_estimate
mb_obs.append(latest_obs)
mb_actions.append((action_ids))
# (MINE) do action, return it to environment, get new obs and reward, store reward
#actions_pp = self.action_processer.process(action_ids) # Actions have changed now need to check: BEFORE: actions.FunctionCall(actions.FUNCTIONS.no_op.id, []) NOW: actions.FUNCTIONS.no_op()
obs_raw = self.envs.step(action_ids)
#obs_raw.reward = reward
latest_obs = self.obs_processer.process(
obs_raw[0]
) # For obs_raw as tuple! #(MINE) =state(t+1). Processes all inputs/obs from all timesteps (and envs)
#print('-->|rewards:', np.round(np.mean(obs_raw[1]), 3))
mb_rewards[:, n] = [t for t in obs_raw[1]]
mb_done[:, n] = [t for t in obs_raw[2]]
#Check for all t (timestep/observation in obs_raw which t has the last state true, meaning it is the last state
# IF MAX_STEPS OR GOAL REACHED
# You can use as below for obs_raw[4] which is success of failure
#print(obs_raw[2])
indx = 0
for t in obs_raw[2]:
if t == True: # done=true
# Put reward in scores
epis_reward = obs_raw[3][indx]['episode']['r']
epis_length = obs_raw[3][indx]['episode']['l']
last_step_r = obs_raw[1][indx]
self._handle_episode_end(
epis_reward, epis_length, last_step_r
) # The printing score process is NOT a parallel process apparrently as you input every reward (t) independently
indx = indx + 1 # finished envs count
# for t in obs_raw:
# if t.last():
# self._handle_episode_end(t)
#print(">> Avg. Reward:",np.round(np.mean(mb_rewards),3))
mb_values[:, -1] = self.agent.get_value(
latest_obs
) # We bootstrap from last step if not terminal! although he doesnt use any check here
n_step_advantage = general_n_step_advantage(
mb_rewards,
mb_values,
self.discount,
mb_done,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0)
full_input = {
# these are transposed because action/obs
# processers return [time, env, ...] shaped arrays
FEATURE_KEYS.advantage:
n_step_advantage.transpose(),
FEATURE_KEYS.value_target:
(n_step_advantage + mb_values[:, :-1]).transpose(
) # if you add to the advantage the value you get the target for your value function training. Check onenote in APL-virtual
}
#(MINE) Probably we combine all experiences from every worker below
full_input.update(self.action_processer.combine_batch(mb_actions))
full_input.update(self.obs_processer.combine_batch(mb_obs))
full_input = {
k: combine_first_dimensions(v)
for k, v in full_input.items()
}
if not self.do_training:
pass
elif self.agent.mode == ACMode.A2C:
self.agent.train(full_input)
elif self.agent.mode == ACMode.PPO:
for epoch in range(self.ppo_par.n_epochs):
self._train_ppo_epoch(full_input)
self.agent.update_theta()
self.latest_obs = latest_obs
self.batch_counter += 1 # It is used only for printing reasons as the outer while loop takes care to stop the number of batches
print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
def run_trained_batch(self):
#gameDisplay.fill((1, 50, 130))
# STATE, ACTION, REWARD, NEXT STATE
# YOU NEED TO DISPLAY FIRST IMAGE HERE AS YOU HAVE RESETED AND THERE ARE OBS THERE AS WELL (YOUR FIRST ONES!)
#sleep(2.0)
latest_obs = self.latest_obs # (MINE) =state(t)
# action = agent(state)
action_ids, value_estimate, representation = self.agent.step_eval(
latest_obs
) # (MINE) AGENT STEP = INPUT TO NN THE CURRENT STATE AND OUTPUT ACTION
print('|actions:', action_ids)
obs_raw = self.envs.step(
action_ids
) # It will also visualize the next observation if all the episodes have ended as after success it retunrs the obs from reset
latest_obs = self.obs_processer.process(
obs_raw[0:-3]
) # Take only the first element which is the rgb image and ignore the reward, done etc
print('-->|rewards:', np.round(np.mean(obs_raw[1]), 3))
# if obs_raw[2]: # done is True
# # for r in obs_raw[1]: # You will double count here as t
# self._handle_episode_end(obs_raw[1]) # The printing score process is NOT a parallel process apparrently as you input every reward (t) independently
self.latest_obs = latest_obs # (MINE) state(t) = state(t+1), the usual s=s'
self.batch_counter += 1
#print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
return obs_raw[0:-3], action_ids[0], value_estimate[0], obs_raw[
1], obs_raw[2]
class Runner(object):
def __init__(
self,
envs,
agent: ActorCriticAgent,
n_steps=5,
discount=0.99,
do_training=True,
ppo_par: PPORunParams = None,
n_envs=1,
policy_type = None
):
self.envs = envs
self.n_envs = n_envs
self.agent = agent
self.obs_processer = ObsProcesser()
self.action_processer = ActionProcesser(dim=8)
self.n_steps = n_steps
self.discount = discount
self.do_training = do_training
self.ppo_par = ppo_par
self.batch_counter = 0
self.episode_counter = 0
self.score = 0.0
# self.policy_type = FullyConvPolicy if ( (policy_type == 'FullyConv') or (policy_type == 'Relational')) else MetaPolicy
if policy_type == 'FullyConv':
self.policy_type = FullyConvPolicy
elif policy_type == 'Relational':
self.policy_type = RelationalPolicy
elif policy_type == 'MetaPolicy':
self.policy_type = MetaPolicy
elif policy_type == 'FullyConv3D':
self.policy_type = FullyConv3DPolicy
elif policy_type == 'AlloAndAlt':
self.policy_type = FullyConvPolicyAlt
elif (policy_type == 'Factored'):
self.policy_type = FactoredPolicy
elif (policy_type == 'FactoredPolicy_PhaseI'):
self.policy_type = FactoredPolicy_PhaseI
elif (policy_type == 'FactoredPolicy_PhaseII'):
self.policy_type = FactoredPolicy_PhaseII
else: print('Unknown Policy')
assert self.agent.mode in [ACMode.A2C, ACMode.PPO]
self.is_ppo = self.agent.mode == ACMode.PPO
if self.is_ppo:
assert ppo_par is not None
# assert n_steps * envs.n_envs % ppo_par.batch_size == 0
# assert n_steps * envs.n_envs >= ppo_par.batch_size
assert n_steps * self.envs.num_envs % ppo_par.batch_size == 0
assert n_steps * self.envs.num_envs >= ppo_par.batch_size
self.ppo_par = ppo_par
def reset(self):
#self.score = 0.0
obs = self.envs.reset()
self.latest_obs = self.obs_processer.process(obs)
def reset_demo(self):
#self.score = 0.0
obs = self.envs.reset()
self.latest_obs = self.obs_processer.process([obs])
def _log_score_to_tb(self, score, length): # log score to tensorboard
summary = tf.Summary()
summary.value.add(tag='sc2/episode_score', simple_value=score)
summary.value.add(tag='sc2/episode_length', simple_value=length)
self.agent.summary_writer.add_summary(summary, self.episode_counter)
def first_nonzero(self, arr, axis):#, invalid_val=self.n_steps):
mask = arr != 0
return np.where(mask.any(axis=axis), mask.argmax(axis=axis), self.n_steps)
def _handle_episode_end(self, timestep, length, last_step_r):
self.score = timestep
# print("\rMean Rewards: {:.2f} Episode: {:d} ".format(
# np.mean(self.ep_rewards[-100:]), self.ep_counter), end="")
# print("\rMean Rewards: {:.2f} Episode: {:d} ".format(
# self.score, self.episode_counter), end="") # To be persistent on the screen it needs to be ran on a main loop, so you print once and just updates the score
# print(">>>>>>>>>>>>>>>episode %d ended. Score %f | Total Steps %d | Last step Reward %f" % (self.episode_counter, self.score, length, last_step_r))
print(">>>>>>>>>>>>>>>episode %d ended. Score %f | Total Steps %d" % (
self.episode_counter, self.score, length))
self._log_score_to_tb(self.score, length) # logging score to tensorboard
self.episode_counter += 1 # Is not used for stopping purposes judt for printing. You train for a number of batches (nsteps+training no matter reset)
#self.reset() # Error if Monitor doesnt have the option to reset without an env to be done (THIS RESETS ALL ENVS!!! YOU NEED remot.send(env.reset) to reset a specific env. Else restart within the env
def _train_ppo_epoch(self, full_input):
total_obs = self.n_steps * self.envs.num_envs
shuffle_idx = np.random.permutation(total_obs)
batches = dict_of_lists_to_list_of_dicst({
k: np.split(v[shuffle_idx], total_obs // self.ppo_par.batch_size)
for k, v in full_input.items()
})
if self.policy_type == MetaPolicy: # We take out the if from the loop so you choose trainer BEFORE getting into the batch loop
for b in batches:
self.agent.train_recurrent(b)
else:
for b in batches:
self.agent.train(b)
def _train_ppo_recurrent_epoch(self, full_input, rnn_state):
# HE SHUFFLES SO BE CAREFUL!!! RECHECK IT: rnn_state might need to get in the full_input
total_obs = self.n_steps * self.envs.num_envs
shuffle_idx = np.random.permutation(total_obs)
batches = dict_of_lists_to_list_of_dicst({
k: np.split(v[shuffle_idx], total_obs // self.ppo_par.batch_size)
for k, v in full_input.items()
})
for b in batches:
self.agent.train_recurrent(b, rnn_state) # IMPORTANT : όταν κανεις training δεν χρειαζεσαι την rnn_State, ξεκινας απο το 0 και αθτη παιρνη την μορφή πουπρεπει να εχει
def run_batch(self):
#(MINE) MAIN LOOP!!!
# The reset is happening through Monitor (except the first one of the first batch (is in hte run_agent)
mb_actions = []
mb_obs = []
mb_values = np.zeros((self.envs.num_envs, self.n_steps + 1), dtype=np.float32)
mb_rewards = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.float32)
mb_done = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.int32)
latest_obs = self.latest_obs # (MINE) =state(t)
for n in range(self.n_steps):
# could calculate value estimate from obs when do training
# but saving values here will make n step reward calculation a bit easier
action_ids, value_estimate = self.agent.step(latest_obs)
# print('|step:', n, '|actions:', action_ids) # (MINE) If you put it after the envs.step the SUCCESS appears at the envs.step so it will appear oddly
# (MINE) Store actions and value estimates for all steps
mb_values[:, n] = value_estimate
mb_obs.append(latest_obs)
mb_actions.append((action_ids))
# (MINE) do action, return it to environment, get new obs and reward, store reward
#actions_pp = self.action_processer.process(action_ids) # Actions have changed now need to check: BEFORE: actions.FunctionCall(actions.FUNCTIONS.no_op.id, []) NOW: actions.FUNCTIONS.no_op()
obs_raw = self.envs.step(action_ids)
#obs_raw.reward = reward
latest_obs = self.obs_processer.process(obs_raw[0]) # For obs_raw as tuple! #(MINE) =state(t+1). Processes all inputs/obs from all timesteps (and envs)
#print('-->|rewards:', np.round(np.mean(obs_raw[1]), 3))
mb_rewards[:, n] = [t for t in obs_raw[1]]
mb_done[:, n] = [t for t in obs_raw[2]]
#Check for all t (timestep/observation in obs_raw which t has the last state true, meaning it is the last state
# IF MAX_STEPS OR GOAL REACHED
if obs_raw[2].any(): # At least one env has done=True
for i in (np.argwhere(obs_raw[2])): # Run the loop for ONLY the envs that have finished
indx = i[0] # i will contain in [] the index of the env that has finished
epis_reward = obs_raw[3][indx]['episode']['r']
epis_length = obs_raw[3][indx]['episode']['l']
last_step_r = obs_raw[1][indx]
self._handle_episode_end(epis_reward, epis_length, last_step_r)
# self.latest_obs[indx] = self.envs
# You can use as below for obs_raw[4] which is success of failure
#print(obs_raw[2])
# indx=0 # env count
# for t in obs_raw[2]: # Monitor returns additional stuff such as epis_reward and epis_length etc apart the obs, r, done, info
# #obs_raw[2] = done = [True, False, False, True,...] each element corresponds to an env
# if t == True: # done=true
# # Put reward in scores
# epis_reward = obs_raw[3][indx]['episode']['r']
# epis_length = obs_raw[3][indx]['episode']['l']
# last_step_r = obs_raw[1][indx]
# self._handle_episode_end(epis_reward, epis_length, last_step_r) # The printing score process is NOT a parallel process apparrently as you input every reward (t) independently
indx = indx + 1 # finished envs count
# for t in obs_raw:
# if t.last():
# self._handle_episode_end(t)
#print(">> Avg. Reward:",np.round(np.mean(mb_rewards),3))
mb_values[:, -1] = self.agent.get_value(latest_obs) # We bootstrap from last step. If not terminal! although he doesnt use any check here
# R, G = calc_rewards(self, mb_rewards, mb_values[:,:-1], mb_values[:,1:], mb_done, self.discount)
# R = R.reshape((self.n_envs, self.n_steps)) # self.n_envs returns 1, self.envs.num_envs
# G = G.reshape((self.n_envs, self.n_steps))
# He replaces the last value with a bootstrap value. E.g. s_t-->V(s_t),s_{lastnstep}-->V(s_{lastnstep}), s_{lastnstep+1}
# and we replace the V(s_{lastnstep}) with V(s_{lastnstep+1})
n_step_advantage = general_nstep_adv_sequential(
mb_rewards,
mb_values,
self.discount,
mb_done,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0, # change lambda to something between 1 and 0 else you do not get the nstep reward calculation
nenvs=self.n_envs,
maxsteps=self.n_steps
)
# n_step_advantage = general_n_step_advantage(
# mb_rewards,
# mb_values,
# self.discount,
# mb_done,
# lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0
# )
# disc_ret = calculate_n_step_reward(
# mb_rewards,
# self.discount,
# mb_values,
# )
full_input = {
# these are transposed because action/obs
# processers return [time, env, ...] shaped arrays CHECK THIS OUT!!!YOU HAVE ALREADY THE TIMESTEP DIM!!!
FEATURE_KEYS.advantage: n_step_advantage.transpose(),#, G.transpose()
FEATURE_KEYS.value_target: (n_step_advantage + mb_values[:, :-1]).transpose()# R.transpose()# (we left out the last element) if you add to the advantage the value you get the target for your value function training. Check onenote in cmu_gym/Next Steps
}
#(MINE) Probably we combine all experiences from every worker below
full_input.update(self.action_processer.combine_batch(mb_actions))
full_input.update(self.obs_processer.combine_batch(mb_obs))
# Below comment it out for LSTM
full_input = {k: combine_first_dimensions(v) for k, v in full_input.items()} # The function takes in nsteps x nenvs x [dims] and combines nsteps*nenvs x [dims].
if not self.do_training:
pass
elif self.agent.mode == ACMode.A2C:
self.agent.train(full_input)
elif self.agent.mode == ACMode.PPO:
for epoch in range(self.ppo_par.n_epochs):
self._train_ppo_epoch(full_input)
self.agent.update_theta()
self.latest_obs = latest_obs
self.batch_counter += 1 # It is used only for printing reasons as the outer while loop takes care to stop the number of batches
print('Update %d finished' % self.batch_counter)
sys.stdout.flush()
def run_factored_batch(self):
#(MINE) MAIN LOOP!!!
# The reset is happening through Monitor (except the first one of the first batch (is in hte run_agent)
mb_actions = []
mb_obs = []
mb_values = np.zeros((self.envs.num_envs, self.n_steps + 1), dtype=np.float32)
mb_values_goal = np.zeros((self.envs.num_envs, self.n_steps + 1), dtype=np.float32)
mb_values_fire = np.zeros((self.envs.num_envs, self.n_steps + 1), dtype=np.float32)
mb_rewards = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.float32)
mb_rewards_goal = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.float32)
mb_rewards_fire = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.float32)
mb_done = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.int32)
latest_obs = self.latest_obs # (MINE) =state(t)
for n in range(self.n_steps):
# could calculate value estimate from obs when do training
# but saving values here will make n step reward calculation a bit easier
action_ids, value_estimate_goal, value_estimate_fire, value_estimate = self.agent.step_factored(latest_obs)
# print('|step:', n, '|actions:', action_ids) # (MINE) If you put it after the envs.step the SUCCESS appears at the envs.step so it will appear oddly
if np.random.random() < 0.20: action_ids = np.array([self.envs.action_space.sample() for _ in range(self.envs.num_envs)])
# (MINE) Store actions and value estimates for all steps
mb_values[:, n] = value_estimate
mb_values_goal[:, n] = value_estimate_goal
mb_values_fire[:, n] = value_estimate_fire
mb_obs.append(latest_obs)
mb_actions.append((action_ids))
# (MINE) do action, return it to environment, get new obs and reward, store reward
#actions_pp = self.action_processer.process(action_ids) # Actions have changed now need to check: BEFORE: actions.FunctionCall(actions.FUNCTIONS.no_op.id, []) NOW: actions.FUNCTIONS.no_op()
obs_raw = self.envs.step(action_ids)
#obs_raw.reward = reward
latest_obs = self.obs_processer.process(obs_raw[0]) # For obs_raw as tuple! #(MINE) =state(t+1). Processes all inputs/obs from all timesteps (and envs)
#print('-->|rewards:', np.round(np.mean(obs_raw[1]), 3))
mb_rewards[:, n] = [t for t in obs_raw[1]]
temp = [t for t in obs_raw[3]]
mb_rewards_goal[:,n] = [d['goal'] for d in temp]
mb_rewards_fire[:, n] = [d['fire'] for d in temp]
mb_done[:, n] = [t for t in obs_raw[2]]
# IF MAX_STEPS OR GOAL REACHED
if obs_raw[2].any(): # At least one env has done=True
for i in (np.argwhere(obs_raw[2])): # Run the loop for ONLY the envs that have finished
indx = i[0]
epis_reward = obs_raw[3][indx]['episode']['r']
epis_length = obs_raw[3][indx]['episode']['l']
last_step_r = obs_raw[1][indx]
self._handle_episode_end(epis_reward, epis_length, last_step_r)
# indx=0 # env count
# for t in obs_raw[2]: # Monitor returns additional stuff such as epis_reward and epis_length etc apart the obs, r, done, info
# #obs_raw[2] = done = [True, False, False, True,...] each element corresponds to an env
# if t == True: # done=true
# # Put reward in scores
# epis_reward = obs_raw[3][indx]['episode']['r']
# epis_length = obs_raw[3][indx]['episode']['l']
# last_step_r = obs_raw[1][indx]
# self._handle_episode_end(epis_reward, epis_length, last_step_r) # The printing score process is NOT a parallel process apparrently as you input every reward (t) independently
# indx = indx + 1 # finished envs count
# for t in obs_raw:
# if t.last():
# self._handle_episode_end(t)
#print(">> Avg. Reward:",np.round(np.mean(mb_rewards),3))
mb_values_goal[:, -1], mb_values_fire[:, -1], mb_values[:, -1] = self.agent.get_factored_value(latest_obs) # We bootstrap from last step. If not terminal! although he doesnt use any check here
# R, G = calc_rewards(self, mb_rewards, mb_values[:,:-1], mb_values[:,1:], mb_done, self.discount)
# R = R.reshape((self.n_envs, self.n_steps)) # self.n_envs returns 1, self.envs.num_envs
# G = G.reshape((self.n_envs, self.n_steps))
# He replaces the last value with a bootstrap value. E.g. s_t-->V(s_t),s_{lastnstep}-->V(s_{lastnstep}), s_{lastnstep+1}
# and we replace the V(s_{lastnstep}) with V(s_{lastnstep+1})
n_step_advantage_goal = general_nstep_adv_sequential(
mb_rewards_goal,
mb_values_goal,
self.discount,
mb_done,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0,
nenvs=self.n_envs,
maxsteps=self.n_steps
)
n_step_advantage_fire = general_nstep_adv_sequential(
mb_rewards_fire,
mb_values_fire,
self.discount,
mb_done,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0,
nenvs=self.n_envs,
maxsteps=self.n_steps
)
# n_step_advantage = n_step_advantage_goal + n_step_advantage_fire
n_step_advantage = general_nstep_adv_sequential(
mb_rewards,
mb_values,
self.discount,
mb_done,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0,
nenvs=self.n_envs,
maxsteps=self.n_steps
)
# disc_ret = calculate_n_step_reward(
# mb_rewards,
# self.discount,
# mb_values,
# )
full_input = {
# these are transposed because action/obs
# processers return [time, env, ...] shaped arrays CHECK THIS OUT!!!YOU HAVE ALREADY THE TIMESTEP DIM!!!
FEATURE_KEYS.advantage: n_step_advantage.transpose(),#, G.transpose()
FEATURE_KEYS.value_target: (n_step_advantage + mb_values[:, :-1]).transpose(),
FEATURE_KEYS.value_target_goal: (n_step_advantage_goal + mb_values_goal[:, :-1]).transpose(),# R.transpose()# (we left out the last element) if you add to the advantage the value you get the target for your value function training. Check onenote in cmu_gym/Next Steps
FEATURE_KEYS.value_target_fire: (n_step_advantage_fire + mb_values_fire[:, :-1]).transpose()
}
# full_input = {
# # these are transposed because action/obs
# # processers return [time, env, ...] shaped arrays CHECK THIS OUT!!!YOU HAVE ALREADY THE TIMESTEP DIM!!!
# FEATURE_KEYS.advantage: n_step_advantage.transpose(),#, G.transpose()
# FEATURE_KEYS.value_target: (n_step_advantage + mb_values[:, :-1]).transpose()# R.transpose()# (we left out the last element) if you add to the advantage the value you get the target for your value function training. Check onenote in cmu_gym/Next Steps
# }
#(MINE) Probably we combine all experiences from every worker below
full_input.update(self.action_processer.combine_batch(mb_actions))
full_input.update(self.obs_processer.combine_batch(mb_obs))
# Below comment it out for LSTM
full_input = {k: combine_first_dimensions(v) for k, v in full_input.items()} # The function takes in nsteps x nenvs x [dims] and combines nsteps*nenvs x [dims].
if not self.do_training:
pass
elif self.agent.mode == ACMode.A2C:
self.agent.train(full_input)
elif self.agent.mode == ACMode.PPO:
for epoch in range(self.ppo_par.n_epochs):
self._train_ppo_epoch(full_input)
self.agent.update_theta()
self.latest_obs = latest_obs
self.batch_counter += 1 # It is used only for printing reasons as the outer while loop takes care to stop the number of batches
print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
def run_meta_batch(self):
mb_actions = []
mb_obs = []
mb_rnns = []
mb_values = np.zeros((self.envs.num_envs, self.n_steps + 1), dtype=np.float32)
mb_rewards = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.float32) # n x d array (ndarray)
mb_done = np.zeros((self.envs.num_envs, self.n_steps), dtype=np.int32)
# EVERYTHING IS HAPPENING ON PARALLEL!!!
r_=np.zeros((self.envs.num_envs, 1), dtype=np.float32) # Instead of 1 you might use n_steps
a_=np.zeros((self.envs.num_envs), dtype=np.int32)
latest_obs = self.latest_obs # (MINE) =state(t)
# rnn_state = self.agent.theta.state_init
rnn_state = self.agent.theta.state_init
for n in range(self.n_steps):
action_ids, value_estimate, rnn_state_new = self.agent.step_recurrent(latest_obs, rnn_state, r_, a_) # Automatically returns [num_envs, outx] for each outx you want
# print('|step:', n, '|actions:', action_ids)
# (MINE) Store actions and value estimates for all steps
mb_values[:, n] = value_estimate
mb_obs.append(latest_obs)
mb_actions.append((action_ids))
# (MINE) do action, return it to environment, get new obs and reward, store reward
obs_raw = self.envs.step(action_ids)
latest_obs = self.obs_processer.process(obs_raw[0]) # For obs_raw as tuple! #(MINE) =state(t+1). Processes all inputs/obs from all timesteps (and envs)
mb_rnns.append(rnn_state)
rnn_state = rnn_state_new
r_ = obs_raw[1] # (nenvs,) but you need (nenvs,1)
r_ = np.reshape(r_,[self.envs.num_envs,1]) # gets into recurrency as [nenvs,1] # The 1 might be used as timestep
a_ = action_ids
mb_rewards[:, n] = [t for t in obs_raw[1]]
mb_done[:, n] = [t for t in obs_raw[2]]
# Shouldnt this part below be OUT of the nstep loop? NO: You check if done=True and you extract the additional info that Monitor outputs
indx=0 # env count
for t in obs_raw[2]: # Monitor returns additional stuff such as epis_reward and epis_length etc apart the obs, r, done, info
# obs_raw[2] = done = [True, False, False, True,...] each element corresponds to an env (index gives the env)
if t == True: # done=true
# Put reward in scores
epis_reward = obs_raw[3][indx]['episode']['r']
epis_length = obs_raw[3][indx]['episode']['l'] # EPISODE LENGTH HERE!!!
last_step_r = obs_raw[1][indx]
self._handle_episode_end(epis_reward, epis_length, last_step_r) # The printing score process is NOT a parallel process apparrently as you input every reward (t) independently
# Here you have to reset the rnn_state of that env (rnn_state has h and c EACH has dims [batch_size x hidden dims]
rnn_state[0][indx] = np.zeros(256)
rnn_state[1][indx] = np.zeros(256)
#reset the relevant r_ and a_
r_[indx] = 0
a_[indx] = 0
indx = indx + 1 # finished envs count
# Below: if all are zeros then you get nsteps
mb_l = self.first_nonzero(mb_done,axis=1) # the last obs s-->sdone are not getting in the training!!!This is because sdone--> ? and R=0
# Substitute 0s with 1 declaring 1 step
# mb_l[mb_l==0] = 1
for b in range(mb_l.shape[0]):
if mb_l[b] < self.n_steps:
mb_l[b] = mb_l[b] + 1 # You start stepping from step 0 to 1
mask = ~(np.ones(mb_rewards.shape).cumsum(axis=1).T > mb_l).T
mb_rewards = mb_rewards*mask
# Below: r + gammaV(s')(1-done) - V(s)// V(s')=mb_values[:,-1] but if its done we dont care if we put the last nstep V or the actual V(s_done+1) as the whole term is gonna be zero
# From V(nstep) estimate the expected reward - what if though we finished the sequence earlier???
# This is for the last obs which you do not store. All other values that will be used as targets are available
# mb_values[:, -1] = self.agent.get_recurrent_value(latest_obs, rnn_state, r_, a_) # Put at last slot the estimated future expected reward for bootstrap the V after the nsteps
mask_v = ~(np.ones(mb_values.shape).cumsum(axis=1).T > mb_l).T
mb_values = mb_values*mask_v
# We take the vector of values after nsteps and we use ONLY the valid ones in the loop below
vec = self.agent.get_recurrent_value(latest_obs, rnn_state, r_, a_)
for b in range(mb_l.shape[0]):
if mb_l[b] == self.n_steps:
mb_values[b, -1] = vec[b] # if the sequence didnt end within nsteps then we add the value so the term gammaVt+1(1-done) is not 0
# Mask below the values that enter the nstep advantage
n_step_advantage = general_nstep_adv_sequential(
mb_rewards,
mb_values,
self.discount,
mb_done,
lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0,
nenvs=self.n_envs,
maxsteps=self.n_steps
)
# n_step_advantage = general_n_step_advantage(
# mb_rewards,
# mb_values,
# self.discount,
# mb_done,
# lambda_par=self.ppo_par.lambda_par if self.is_ppo else 1.0
# )
# prev_rewards = [0] + mb_rewards[:, :-1]#.tolist() # from the rewards you take out the last element and replace it with 0
prev_rewards = np.c_[np.zeros((self.envs.num_envs, 1), dtype=np.float32), mb_rewards[:, :-1]]
# Below we add one zero action element and we take out the a_t so we get at=0:t-1
prev_actions = [np.zeros((self.envs.num_envs), dtype=np.int32)] + mb_actions[:-1] # You have to pad this probably to have equal lengths with your data in terms of nsteps
full_input = {
FEATURE_KEYS.advantage: n_step_advantage.transpose(),
FEATURE_KEYS.value_target: (n_step_advantage + mb_values[:, :-1]).transpose() # NETWORK TARGET (A+V=R)
}
full_input.update(self.action_processer.combine_batch(mb_actions))
full_input.update(self.obs_processer.combine_batch(mb_obs))
# full_input.update(self.action_processer.combine_batch(prev_actions)) # THIS FEEDS AGAIN THE SELECTED_ID_ACTION PLACEHOLDER!!!!
# full_input.update(self.action_processer.combine_batch(prev_rewards)) # THIS FEEDS AGAIN THE SELECTED_ID_ACTION PLACEHOLDER!!!!
# Below: [maxsteps x batch] --> [batch x maxsteps]
full_input = {k: np.swapaxes(v, 0, 1) for k, v in full_input.items()} # obs should be first trasnposed and then combine else you loose the time order
full_input = {k: combine_first_dimensions(v) for k, v in full_input.items()} # YOU CAN COMMENT THIS AND UNCOMMENT BELOW
# full_input = {k: np.swapaxes(v,0,1) for k, v in full_input.items()}
if not self.do_training:
pass
elif self.agent.mode == ACMode.A2C:
if self.policy_type == MetaPolicy:
self.agent.train_recurrent(full_input,mb_l,prev_rewards,prev_actions)
else:
self.agent.train(full_input)
elif self.agent.mode == ACMode.PPO:
for epoch in range(self.ppo_par.n_epochs):
self._train_ppo_epoch(full_input)
self.agent.update_theta()
self.latest_obs = latest_obs
self.batch_counter += 1
print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
def run_trained_batch(self):
latest_obs = self.latest_obs # (MINE) =state(t)
action_ids, value_estimate, fc, action_probs = self.agent.step_eval(latest_obs) # (MINE) AGENT STEP = INPUT TO NN THE CURRENT STATE AND OUTPUT ACTION
print('|actions:', action_ids)
obs_raw = self.envs.step(action_ids[0]) # It will also visualize the next observation if all the episodes have ended as after success it retunrs the obs from reset
latest_obs = self.obs_processer.process(obs_raw[0:-3]) # Take only the first element which is the rgb image and ignore the reward, done etc
print('-->|rewards:', np.round(np.mean(obs_raw[1]), 3))
# if obs_raw[2]: # done is True
# # for r in obs_raw[1]: # You will double count here as t
# self._handle_episode_end(obs_raw[1]) # The printing score process is NOT a parallel process apparrently as you input every reward (t) independently
self.latest_obs = latest_obs # (MINE) state(t) = state(t+1), the usual s=s'
self.batch_counter += 1
#print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
return obs_raw[0:-3], action_ids[0], value_estimate[0], obs_raw[1], obs_raw[2], obs_raw[3], fc, action_probs
def run_trained_factored_batch(self):
latest_obs = self.latest_obs # (MINE) =state(t)
action_ids, value_estimate, value_estimate_goal, value_estimate_fire, fc, action_probs = self.agent.step_eval_factored(latest_obs) # (MINE) AGENT STEP = INPUT TO NN THE CURRENT STATE AND OUTPUT ACTION
print('|actions:', action_ids)
obs_raw = self.envs.step(action_ids) # It will also visualize the next observation if all the episodes have ended as after success it retunrs the obs from reset
latest_obs = self.obs_processer.process(obs_raw[0:-3]) # Take only the first element which is the rgb image and ignore the reward, done etc
print('-->|rewards:', np.round(np.mean(obs_raw[1]), 3))
self.latest_obs = latest_obs # (MINE) state(t) = state(t+1), the usual s=s'
self.batch_counter += 1
#print('Batch %d finished' % self.batch_counter)
sys.stdout.flush()
return obs_raw[0:-3], action_ids[0], value_estimate[0], value_estimate_goal[0], value_estimate_fire[0], obs_raw[1], obs_raw[2], obs_raw[3], fc, action_probs