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main.py
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import copy
import glob
import os
import time
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from arguments import get_args
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
from baselines.common.vec_env.vec_normalize import VecNormalize
from envs import make_env
from kfac import KFACOptimizer
from model import CNNPolicy, MLPPolicy
from storage import RolloutStorage
from visualize import visdom_plot
args = get_args()
assert args.algo in ['a2c', 'ppo', 'acktr']
if args.recurrent_policy:
assert args.algo in ['a2c', 'ppo'], \
'Recurrent policy is not implemented for ACKTR'
# NUM ENVS GIVEN IF IS MULTITASK OR NOT
num_envs = len(args.env_name)
print("GONNA TRAIN", num_envs, "GAMES!")
args.num_processes *= num_envs
if args.transfer:
print("LET'S MAKE A TRANFER POLICY")
factor = 0.1
else:
factor = 1
num_updates = int((int(args.num_frames) * num_envs // args.num_steps // args.num_processes) * factor)
print("DURING", num_updates, "STEPS!")
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
try:
os.makedirs(args.log_dir)
except OSError:
files = glob.glob(os.path.join(args.log_dir, '*.monitor.csv'))
for f in files:
os.remove(f)
def main():
print("#######")
print("WARNING: All rewards are clipped or normalized so you need to use a monitor (see envs.py) or visdom plot to get true rewards")
print("#######")
os.environ['OMP_NUM_THREADS'] = '1'
if args.vis:
from visdom import Visdom
viz = Visdom()
win = None
extra = "_".join([ name[:4] for name in args.env_name ])
if args.att != 'None':
extra = args.att+'_'+extra
if args.transfer:
extra = 'transfer_'+extra
# MODIFIED TO ACCEPT MULTI-TASK
num_proc = args.num_processes // num_envs
envs = [make_env(args.env_name[i//num_proc], args.seed, i, args.log_dir+extra+'/')
for i in range(args.num_processes)]
if args.num_processes > 1:
envs = SubprocVecEnv(envs)
else:
envs = DummyVecEnv(envs)
if len(envs.observation_space.shape) == 1:
envs = VecNormalize(envs)
obs_shape = envs.observation_space.shape
obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:])
print(envs.observation_space.shape)
if args.load_model != None:
actor_critic, ob_rms = \
torch.load(args.load_model)
elif len(envs.observation_space.shape) == 3:
actor_critic = CNNPolicy(obs_shape[0], envs.action_space, args.recurrent_policy, args.att)
else:
assert not args.recurrent_policy, \
"Recurrent policy is not implemented for the MLP controller"
print("OI")
actor_critic = MLPPolicy(obs_shape[0], envs.action_space)
if envs.action_space.__class__.__name__ == "Discrete":
action_shape = 1
else:
action_shape = envs.action_space.shape[0]
if args.cuda:
actor_critic.cuda()
if args.algo == 'a2c':
optimizer = optim.RMSprop(actor_critic.parameters(), args.lr, eps=args.eps, alpha=args.alpha)
elif args.algo == 'ppo':
optimizer = optim.Adam(actor_critic.parameters(), args.lr, eps=args.eps)
elif args.algo == 'acktr':
optimizer = KFACOptimizer(actor_critic)
rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape, envs.action_space, actor_critic.state_size)
current_obs = torch.zeros(args.num_processes, *obs_shape)
def update_current_obs(obs):
shape_dim0 = envs.observation_space.shape[0]
#print(shape_dim0,"<-------------- observation shape")
obs = torch.from_numpy(obs).float()
if args.num_stack > 1:
current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:]
current_obs[:, -shape_dim0:] = obs
obs = envs.reset()
update_current_obs(obs)
rollouts.observations[0].copy_(current_obs)
# These variables are used to compute average rewards for all processes.
episode_rewards = torch.zeros([args.num_processes, 1])
final_rewards = torch.zeros([args.num_processes, 1])
if args.cuda:
current_obs = current_obs.cuda()
rollouts.cuda()
start = time.time()
for j in range(num_updates):
for step in range(args.num_steps):
# Sample actions
value, action, action_log_prob, states = actor_critic.act(Variable(rollouts.observations[step], volatile=True),
Variable(rollouts.states[step], volatile=True),
Variable(rollouts.masks[step], volatile=True))
cpu_actions = action.data.squeeze(1).cpu().numpy()
# Obser reward and next obs
obs, reward, done, info = envs.step(cpu_actions)
reward = torch.from_numpy(np.expand_dims(np.stack(reward), 1)).float()
episode_rewards += reward
# If done then clean the history of observations.
masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done])
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
if args.cuda:
masks = masks.cuda()
if current_obs.dim() == 4:
current_obs *= masks.unsqueeze(2).unsqueeze(2)
else:
current_obs *= masks
update_current_obs(obs)
rollouts.insert(step, current_obs, states.data, action.data, action_log_prob.data, value.data, reward, masks)
next_value = actor_critic(Variable(rollouts.observations[-1], volatile=True),
Variable(rollouts.states[-1], volatile=True),
Variable(rollouts.masks[-1], volatile=True))[0].data
rollouts.compute_returns(next_value, args.use_gae, args.gamma, args.tau)
if args.algo in ['a2c', 'acktr']:
values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(Variable(rollouts.observations[:-1].view(-1, *obs_shape)),
Variable(rollouts.states[0].view(-1, actor_critic.state_size)),
Variable(rollouts.masks[:-1].view(-1, 1)),
Variable(rollouts.actions.view(-1, action_shape)))
values = values.view(args.num_steps, args.num_processes, 1)
action_log_probs = action_log_probs.view(args.num_steps, args.num_processes, 1)
advantages = Variable(rollouts.returns[:-1]) - values
value_loss = advantages.pow(2).mean()
action_loss = -(Variable(advantages.data) * action_log_probs).mean()
if args.algo == 'acktr' and optimizer.steps % optimizer.Ts == 0:
# Sampled fisher, see Martens 2014
actor_critic.zero_grad()
pg_fisher_loss = -action_log_probs.mean()
value_noise = Variable(torch.randn(values.size()))
if args.cuda:
value_noise = value_noise.cuda()
sample_values = values + value_noise
vf_fisher_loss = -(values - Variable(sample_values.data)).pow(2).mean()
fisher_loss = pg_fisher_loss + vf_fisher_loss
optimizer.acc_stats = True
fisher_loss.backward(retain_graph=True)
optimizer.acc_stats = False
optimizer.zero_grad()
(value_loss * args.value_loss_coef + action_loss - dist_entropy * args.entropy_coef).backward()
if args.algo == 'a2c':
nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm)
optimizer.step()
elif args.algo == 'ppo':
advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1]
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-5)
for e in range(args.ppo_epoch):
if args.recurrent_policy:
data_generator = rollouts.recurrent_generator(advantages,
args.num_mini_batch)
else:
data_generator = rollouts.feed_forward_generator(advantages,
args.num_mini_batch)
for sample in data_generator:
observations_batch, states_batch, actions_batch, \
return_batch, masks_batch, old_action_log_probs_batch, \
adv_targ = sample
# Reshape to do in a single forward pass for all steps
values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(Variable(observations_batch),
Variable(states_batch),
Variable(masks_batch),
Variable(actions_batch))
adv_targ = Variable(adv_targ)
ratio = torch.exp(action_log_probs - Variable(old_action_log_probs_batch))
surr1 = ratio * adv_targ
surr2 = torch.clamp(ratio, 1.0 - args.clip_param, 1.0 + args.clip_param) * adv_targ
action_loss = -torch.min(surr1, surr2).mean() # PPO's pessimistic surrogate (L^CLIP)
value_loss = (Variable(return_batch) - values).pow(2).mean()
optimizer.zero_grad()
(value_loss + action_loss - dist_entropy * args.entropy_coef).backward()
nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm)
optimizer.step()
rollouts.after_update()
if j % args.save_interval == 0 and args.save_dir != "":
save_path = os.path.join(args.save_dir, args.algo)
try:
os.makedirs(save_path)
except OSError:
pass
# A really ugly way to save a model to CPU
save_model = actor_critic
if args.cuda:
save_model = copy.deepcopy(actor_critic).cpu()
save_model = [save_model,
hasattr(envs, 'ob_rms') and envs.ob_rms or None]
if args.att != 'None':
extra = args.att
else:
extra = ''
if args.transfer:
extra += '_transfer'
torch.save(save_model, os.path.join(save_path, extra+"_".join(args.env_name)+'_'+str(args.seed)+ ".pt"))
if j % args.log_interval == 0:
end = time.time()
total_num_steps = (j + 1) * args.num_processes * args.num_steps
print("Updates {}, num timesteps {}, FPS {}, mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}".
format(j, total_num_steps,
int(total_num_steps / (end - start)),
final_rewards.mean(),
final_rewards.median(),
final_rewards.min(),
final_rewards.max(), dist_entropy.data[0],
value_loss.data[0], action_loss.data[0]))
if args.vis and j % args.vis_interval == 0:
try:
# Sometimes monitor doesn't properly flush the outputs
win = visdom_plot(viz, win, args.log_dir, args.env_name, args.algo)
except IOError:
pass
if __name__ == "__main__":
main()