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Reduce memory usage (2-3x times) #20

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dfefabb
Merge pull request #1 from jcwleo/master
kslazarev Jan 21, 2019
727bc4f
Reduce memory usage (2-3x times). Replace state matrices (total_next_…
kslazarev Jan 21, 2019
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57 changes: 32 additions & 25 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,7 +112,7 @@ def main():
parent_conns.append(parent_conn)
child_conns.append(child_conn)

states = np.zeros([num_worker, 4, 84, 84])
states = np.zeros([num_worker, 4, 84, 84], dtype='float32')

sample_episode = 0
sample_rall = 0
Expand All @@ -124,61 +124,65 @@ def main():

# normalize obs
print('Start to initailize observation normalization parameter.....')
next_obs = []
next_obs = np.zeros([num_worker * num_step, 1, 84, 84])
for step in range(num_step * pre_obs_norm_step):
actions = np.random.randint(0, output_size, size=(num_worker,))

for parent_conn, action in zip(parent_conns, actions):
parent_conn.send(action)

for parent_conn in parent_conns:
for idx, parent_conn in enumerate(parent_conns):
s, r, d, rd, lr = parent_conn.recv()
next_obs.append(s[3, :, :].reshape([1, 84, 84]))
next_obs[(step % num_step) * num_worker + idx, 0, :, :] = s[3, :, :]

if len(next_obs) % (num_step * num_worker) == 0:
next_obs = np.stack(next_obs)
if (step % num_step) == num_step - 1:
obs_rms.update(next_obs)
next_obs = []
next_obs = np.zeros([num_worker * num_step, 1, 84, 84])
print('End to initalize...')

while True:
total_state, total_reward, total_done, total_next_state, total_action, total_int_reward, total_next_obs, total_ext_values, total_int_values, total_policy, total_policy_np = \
[], [], [], [], [], [], [], [], [], [], []
total_state = np.zeros([num_worker * num_step, 4, 84, 84], dtype='float32')
total_next_obs = np.zeros([num_worker * num_step, 1, 84, 84])
total_reward, total_done, total_next_state, total_action, total_int_reward, total_ext_values, total_int_values, total_policy, total_policy_np = \
[], [], [], [], [], [], [], [], []
global_step += (num_worker * num_step)
global_update += 1

# Step 1. n-step rollout
for _ in range(num_step):
actions, value_ext, value_int, policy = agent.get_action(np.float32(states) / 255.)
for step in range(num_step):
actions, value_ext, value_int, policy = agent.get_action(states / 255.)

for parent_conn, action in zip(parent_conns, actions):
parent_conn.send(action)

next_states, rewards, dones, real_dones, log_rewards, next_obs = [], [], [], [], [], []
for parent_conn in parent_conns:
next_obs = np.zeros([num_worker, 1, 84, 84])
next_states = np.zeros([num_worker, 4, 84, 84])
rewards, dones, real_dones, log_rewards = [], [], [], []
for idx, parent_conn in enumerate(parent_conns):
s, r, d, rd, lr = parent_conn.recv()
next_states.append(s)
next_states[idx] = s
rewards.append(r)
dones.append(d)
real_dones.append(rd)
log_rewards.append(lr)
next_obs.append(s[3, :, :].reshape([1, 84, 84]))
next_obs[idx, 0] = s[3, :, :]
total_next_obs[idx * num_step + step, 0] = s[3, :, :]

next_states = np.stack(next_states)
rewards = np.hstack(rewards)
dones = np.hstack(dones)
real_dones = np.hstack(real_dones)
next_obs = np.stack(next_obs)

# total reward = int reward + ext Reward
intrinsic_reward = agent.compute_intrinsic_reward(
((next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5))
next_obs -= obs_rms.mean
next_obs /= np.sqrt(obs_rms.var)
next_obs.clip(-5, 5, out=next_obs)
intrinsic_reward = agent.compute_intrinsic_reward(next_obs)
intrinsic_reward = np.hstack(intrinsic_reward)
sample_i_rall += intrinsic_reward[sample_env_idx]

total_next_obs.append(next_obs)
for idx, state in enumerate(states):
total_state[idx * num_step + step] = state
total_int_reward.append(intrinsic_reward)
total_state.append(states)
total_reward.append(rewards)
total_done.append(dones)
total_action.append(actions)
Expand Down Expand Up @@ -207,11 +211,9 @@ def main():
total_int_values.append(value_int)
# --------------------------------------------------

total_state = np.stack(total_state).transpose([1, 0, 2, 3, 4]).reshape([-1, 4, 84, 84])
total_reward = np.stack(total_reward).transpose().clip(-1, 1)
total_action = np.stack(total_action).transpose().reshape([-1])
total_done = np.stack(total_done).transpose()
total_next_obs = np.stack(total_next_obs).transpose([1, 0, 2, 3, 4]).reshape([-1, 1, 84, 84])
total_ext_values = np.stack(total_ext_values).transpose()
total_int_values = np.stack(total_int_values).transpose()
total_logging_policy = np.vstack(total_policy_np)
Expand Down Expand Up @@ -260,8 +262,13 @@ def main():
# -----------------------------------------------

# Step 5. Training!
agent.train_model(np.float32(total_state) / 255., ext_target, int_target, total_action,
total_adv, ((total_next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5),
total_state /= 255.
total_next_obs -= obs_rms.mean
total_next_obs /= np.sqrt(obs_rms.var)
total_next_obs.clip(-5, 5, out=total_next_obs)

agent.train_model(total_state, ext_target, int_target, total_action,
total_adv, total_next_obs,
total_policy)

if global_step % (num_worker * num_step * 100) == 0:
Expand Down