-
Notifications
You must be signed in to change notification settings - Fork 2
/
train_atari.py
177 lines (140 loc) · 7.12 KB
/
train_atari.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import argparse
import os
import gym
import time
from a2c.model import ActorCritic
from a2c.monitor import Monitor
from a2c.multiprocessing_env import SubprocVecEnv, VecPyTorch, VecPyTorchFrameStack
from a2c.wrappers import *
import torch
import torch.optim as optim
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def parse_args():
parser = argparse.ArgumentParser("A2C experiments for Atari games")
parser.add_argument("--seed", type=int, default=42, help="which seed to use")
# Environment
parser.add_argument("--env", type=str, default="BreakoutNoFrameskip-v4", help="name of the game")
# Core A2C parameters
parser.add_argument("--actor-loss-coefficient", type=float, default=1.0, help="actor loss coefficient")
parser.add_argument("--critic-loss-coefficient", type=float, default=0.5, help="critic loss coefficient")
parser.add_argument("--entropy-loss-coefficient", type=float, default=0.01, help="entropy loss coefficient")
parser.add_argument("--lr", type=float, default=7e-4, help="learning rate for the RMSprop optimizer")
parser.add_argument("--alpha", type=float, default=0.99, help="alpha term the RMSprop optimizer")
parser.add_argument("--eps", type=float, default=1e-5, help="eps term for the RMSprop optimizer") # instead of 1e-3 due to different RMSprop implementation in PyTorch than Tensorflow
parser.add_argument("--max-grad-norm", type=float, default=0.5, help="maximum norm of gradients")
parser.add_argument("--num_steps", type=int, default=5, help="number of forward steps")
parser.add_argument("--num-envs", type=int, default=16, help="number of processes for environments")
parser.add_argument("--gamma", type=float, default=0.99, help="discount factor")
parser.add_argument("--num-frames", type=int, default=int(10e6),
help="total number of steps to run the environment for")
parser.add_argument("--log-dir", type=str, default="logs", help="where to save log files")
parser.add_argument("--save-freq", type=int, default=0, help="updates between saving models (default 0 => no save)")
# Reporting
parser.add_argument("--print-freq", type=int, default=1000, help="evaluation frequency.")
return parser.parse_args()
def compute_returns(next_value, rewards, masks, gamma):
r = next_value
returns = []
for step in reversed(range(len(rewards))):
r = rewards[step] + gamma * r * masks[step]
returns.insert(0, r)
return returns
def make_env(seed, rank):
def _thunk():
env = gym.make(args.env)
env.seed(seed + rank)
assert "NoFrameskip" in args.env, "Require environment with no frameskip"
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
allow_early_resets = False
log_dir = args.log_dir
assert args.log_dir is not None, "Log directory required for Monitor! (which is required for episodic return reporting)"
try:
os.mkdir(log_dir)
except:
pass
env = Monitor(env, os.path.join(log_dir, str(rank)), allow_early_resets=allow_early_resets)
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env)
# env = PyTorchFrame(env)
env = ClipRewardEnv(env)
# env = FrameStack(env, 4)
obs_shape = env.observation_space.shape
if len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = TransposeImage(env)
return env
return _thunk
def make_envs():
envs = [make_env(args.seed, i) for i in range(args.num_envs)]
envs = SubprocVecEnv(envs)
envs = VecPyTorch(envs, device)
envs = VecPyTorchFrameStack(envs, 4, device)
return envs
if __name__ == '__main__':
args = parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
envs = make_envs()
actor_critic = ActorCritic(envs.observation_space, envs.action_space).to(device)
optimizer = optim.RMSprop(actor_critic.parameters(), lr=args.lr, eps=args.eps, alpha=args.alpha)
num_updates = args.num_frames // args.num_steps // args.num_envs
observation = envs.reset()
start = time.time()
episode_rewards = deque(maxlen=10)
for update in range(num_updates):
log_probs = []
values = []
rewards = []
actions = []
masks = []
entropies = []
for step in range(args.num_steps):
observation = observation.to(device) / 255.
actor, value = actor_critic(observation)
action = actor.sample()
next_observation, reward, done, infos = envs.step(action.unsqueeze(1))
for info in infos:
if 'episode' in info.keys():
episode_rewards.append(info['episode']['r'])
log_prob = actor.log_prob(action)
entropy = actor.entropy()
mask = torch.from_numpy(1.0 - done).to(device).float()
entropies.append(actor.entropy())
log_probs.append(log_prob)
values.append(value.squeeze())
rewards.append(reward.to(device).squeeze())
masks.append(mask)
observation = next_observation
next_observation = next_observation.to(device).float() / 255.
with torch.no_grad():
_, next_values = actor_critic(next_observation)
returns = compute_returns(next_values.squeeze(), rewards, masks, args.gamma)
returns = torch.cat(returns)
log_probs = torch.cat(log_probs)
values = torch.cat(values)
entropies = torch.cat(entropies)
advantages = returns - values
actor_loss = -(log_probs * advantages.detach()).mean()
critic_loss = advantages.pow(2).mean()
entropy_loss = entropies.mean()
loss = args.actor_loss_coefficient * actor_loss + \
args.critic_loss_coefficient * critic_loss - \
args.entropy_loss_coefficient * entropy_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(actor_critic.parameters(), args.max_grad_norm)
optimizer.step()
if len(episode_rewards) > 1 and update % args.print_freq == 0:
end = time.time()
total_num_steps = (update + 1) * args.num_envs * args.num_steps
print("********************************************************")
print("update: {0}, total steps: {1}, FPS: {2}".format(update, total_num_steps, int(total_num_steps / (end - start))))
print("mean/median reward: {:.1f}/{:.1f}".format(np.mean(episode_rewards), np.median(episode_rewards)))
print("min/max reward: {:.1f}/{:.1f}".format(np.min(episode_rewards), np.max(episode_rewards)))
print("actor loss: {:.5f}, critic loss: {:.5f}, entropy: {:.5f}".format(actor_loss.item(), critic_loss.item(), entropy_loss.item()))
print("********************************************************")
if args.save_freq > 0 and update % args.save_freq == 0:
torch.save(actor_critic.state_dict(), './{}-{}.pth'.format(args.env, update))