/
dqn.py
216 lines (185 loc) · 6.89 KB
/
dqn.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
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from pokeagent.models.memory import ReplayMemory
from pokeagent.models.agent import Agent
class DQN(nn.Module):
"""
Base DQN class for running RL models.
Args:
embedding_size: size of input embedding
"""
def __init__(self, embedding_size, num_actions=4):
super().__init__()
self._embedding_size = embedding_size
self._num_actions = num_actions
# mario: (4, 84, 84)
self.net = nn.Sequential(
nn.Linear(embedding_size, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self._num_actions),
# nn.Conv2d(self._num_frames, out_channels=32, kernel_size=8, stride=4),
# nn.ReLU(),
# nn.Conv2d(32, out_channels=64, kernel_size=4, stride=2),
# nn.ReLU(),
# nn.Conv2d(64, out_channels=64, kernel_size=3, stride=1),
# nn.ReLU(),
# nn.Flatten(),
# nn.Linear(64 * 7 * 7, 512),
# nn.ReLU(),
# nn.Linear(512, self._num_actions),
)
def forward(self, obs):
actions = self.net(obs)
return actions
class DQNAgent(Agent):
"""
Agent using DQN (Double DQN) implementation to make choices.
"""
def __init__(
self,
embedding_size,
num_actions,
device,
evaluate,
batch_size=16,
gamma=0.9,
lr=1e-4,
epsilon_decay=0.99999975,
epsilon_min=0.1,
save_model_iter=10000,
sync_model_iter=10000,
update_freq=5,
warmup=10000,
name='dqn',
save_dir="../output/",
):
self._embedding_size = embedding_size
self._num_actions = num_actions
self._gamma = gamma
self._batch_size = batch_size
self._replay_memory = ReplayMemory()
self._epsilon = 1.0
self._epsilon_decay = epsilon_decay
self._epsilon_min = epsilon_min
self._target_network = DQN(embedding_size, num_actions)
self._policy_network = DQN(embedding_size, num_actions)
self._target_network.load_state_dict(self._policy_network.state_dict())
self._curr_step = 0
self._save_model_iter = save_model_iter
self._sync_model_iter = sync_model_iter
self._warmup = warmup
self._save_dir = save_dir
self._update_freq = update_freq
self._device = device
self._evaluate = evaluate
self._name = name
self._target_network = self._target_network.to(device)
self._policy_network = self._policy_network.to(device)
self._optimizer = torch.optim.Adam(self._policy_network.parameters(), lr=lr)
def cache(self, state, action, reward, next_state, done):
self._replay_memory.store(state, action, reward, next_state, done)
def td_estimate(self, state, action):
q_values = self._policy_network(state)
q_values = q_values.gather(1, action.unsqueeze(1)).squeeze()
return q_values
def td_target(self, reward, next_state, done):
with torch.no_grad():
target_next_q = self._policy_network(next_state)
best_action = torch.argmax(target_next_q, axis=1)
q_values = target_next_q.gather(1, best_action.unsqueeze(1)).squeeze()
return reward.squeeze() + self._gamma * (1 - done.squeeze()) * q_values
def sync_target(self):
"""
Sync weights between current Q network and target
"""
self._target_network.load_state_dict(self._policy_network.state_dict())
def optimize(self):
"""
Optimize TD error/loss from DQN agent
Returns V(s) and R
"""
# sync or save model
if self._curr_step < self._warmup:
return None, None
if self._curr_step % self._update_freq:
return None, None
if self._curr_step % self._save_model_iter == 0:
self.save()
if self._curr_step % self._sync_model_iter == 0:
self.sync_target()
# batch of samples from experience
s, a, r, sprime, done = self._replay_memory.sample(self._batch_size)
# normalize pixels and send to gpu
s = np.array(s) / 255.0
sprime = np.array(sprime) / 255.0
s = torch.from_numpy(s).float().to(self._device)
sprime = torch.from_numpy(sprime).float().to(self._device)
r = torch.from_numpy(r).float().to(self._device)
a = torch.from_numpy(a).to(self._device)
done = torch.from_numpy(done).int().to(self._device)
# compute td targets and estimate for loss
td_estimate = self.td_estimate(s, a)
td_target = self.td_target(r, sprime, done)
if td_target.shape != td_estimate.shape:
pass
# compute loss and backpropogate
loss = F.smooth_l1_loss(td_estimate, td_target)
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
del s, sprime
return (td_estimate.mean().item(), loss.item())
def action(self, state):
"""
Select epsilon-greedy action to take
"""
with torch.no_grad():
choose = np.random.uniform(0, 1)
if choose < self._epsilon and not self._evaluate:
action = np.random.randint(0, self._num_actions)
else:
# state = np.transpose(state, (0, 3, 1, 2))
state = torch.from_numpy(state).float().to(self._device)
state /= 255.0
q_values = self._policy_network(state)
# print()
action = torch.argmax(q_values, axis=0).item()
self._epsilon *= self._epsilon_decay
self._epsilon = max(self._epsilon_min, self._epsilon)
self._curr_step += 1
return action
def save(self):
save_path = (
self._save_dir
/ f"{self._name}_dqn_net_{int(self._curr_step // self._save_model_iter)}.chkpt"
)
torch.save(
dict(
model=self._policy_network.state_dict(), exploration_rate=self._epsilon
),
save_path,
)
print(f"DQN saved to {save_path} at step {self._curr_step}")
def save_all(self):
save_path = (
self._save_dir
/ f"{self._name}_dqn_net_final.chkpt"
)
torch.save(
dict(
model=self._policy_network.state_dict(), exploration_rate=self._epsilon
),
save_path,
)
print(f"DQN saved to {save_path} after training finished")
def load(self, path, device):
self._policy_network.load_state_dict(
torch.load(path, map_location=device)["model"]
)
print(f"DQN loaded from to {path} at step {self._curr_step}")