def test_combine_first_dimensions(self):
x = np.random.randint(100, size=(3, 5, 7, 9))
result = combine_first_dimensions(x)
assert result.shape == (15, 7, 9)
assert np.all(result[0] == x[0, 0])
assert np.all(result[6] == x[1, 1])
assert np.all(result[7] == x[1, 2])
x = np.random.randint(10, size=(3, 5))
assert np.all(combine_first_dimensions(x) == x.flatten())
def train(self,
n_step_rewards,
mb_obs_combined,
mb_actions_combined
):
feed_dict = {
self.ph_value_target: n_step_rewards,
self.ph_selected_spatial_action: mb_actions_combined["spatial_action"],
self.ph_selected_action_id: mb_actions_combined["action_id"],
self.ph_is_spatial_action_available: mb_actions_combined["is_spatial_action_available"]
}
feed_dict.update(self.obs_to_feeddict(mb_obs_combined))
# treat each timestep as a separate observation, so batch_size will become batch_size * timesteps
feed_dict = {k: combine_first_dimensions(v) for k, v in feed_dict.items()}
ops = [self.train_op]
write_all_summaries = (
(self.train_step % self.all_summary_freq == 0) and
self.summary_path is not None
)
write_scalar_summaries = (
(self.train_step % self.scalar_summary_freq == 0) and
self.summary_path is not None
)
if write_all_summaries:
ops.append(self.all_summary_op)
elif write_scalar_summaries:
ops.append(self.scalar_summary_op)
r = self.sess.run(ops, feed_dict)
if write_all_summaries or write_scalar_summaries:
self.summary_writer.add_summary(r[-1], global_step=self.train_step)
self.train_step += 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_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()
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_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)
self.sprint.print(
["values,rewards=", mb_values.shape, mb_rewards.shape])
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)
self.aprint.print(
["test act_print", action_ids, spatial_action_2ds, actions_pp])
obs_raw = self.envs.step(actions_pp)
latest_obs = self.obs_processer.process(obs_raw)
self.oprint.print([
"test obs_print", [(k, v.shape) for k, v in latest_obs.items()]
])
mb_rewards[:, n] = [t.reward for t in obs_raw]
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_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()
}
self.batch_print.print(
["full_input=", [(k, v.shape) 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()
def run_batch(self):
"""run_batch
Run a batch of the training, building up a list of actions, observations,
values of those actions and the rewards given.
"""
if self.number_episodes == self.episode_counter:
print("Max number episodes reached. Quitting.")
return False
# Define variables to store the actions, observations, values and rewards in.
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
# For the number of steps, save the relevant data each step.
# When finished, deal with the episode end.
for n_step in range(self.n_steps):
# Save the value estimate here, to make the n step reward calculation easier.
action_id, spatial_action_2d, value_estimate = self.agent.step(latest_obs)
mb_values[:, n_step] = value_estimate
mb_obs.append(latest_obs)
mb_actions.append((action_id, spatial_action_2d))
actions_pp = self.action_processor.process(action_id, spatial_action_2d)
obs_raw = self.envs.step(actions_pp)
latest_obs = self.obs_processor.process(obs_raw)
mb_rewards[:, n_step] = [t.reward for t in obs_raw]
for timestep in obs_raw:
if timestep.last():
self._handle_episode_end(timestep)
mb_values[:, -1] = self.agent.get_value(latest_obs)
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
# processors 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_processor.combine_batch(mb_actions))
full_input.update(self.obs_processor.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)
self.latest_obs = latest_obs
self.batch_counter += 1
sys.stdout.flush()
return True
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_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()