-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathrl_agent.py
148 lines (120 loc) · 5.69 KB
/
rl_agent.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
import numpy as np
import math
import refs.tiles3 as tc
import itertools
class CartPoleTileCoder:
def __init__(self, iht_size = 4096, num_tilings = 32, num_tiles = 8):
self.num_tilings = num_tilings
self.num_tiles = num_tiles
self.iht = tc.IHT(iht_size)
def get_tiles(self, observation):
observation_scaled = np.zeros((4,))
observation_scaled = np.divide(observation.reshape(4,), 0.25*np.array([4, 8, np.pi/2, np.pi])) + 1/2
tiles = tc.tileswrap(self.iht, self.num_tilings, observation_scaled, wrapwidths = [False, False, False, False])
return np.array(tiles)
def compute_softmax_prob(actor_w, tiles):
state_action_preferences = []
state_action_preferences = actor_w[:, tiles].sum(axis = 1)
#print("State action preferences", state_action_preferences, np.shape(state_action_preferences))
c = np.max(state_action_preferences)
numerator = np.exp(state_action_preferences-c)
denominator = np.sum(numerator)
softmax_prob = np.divide(numerator, denominator)
return softmax_prob
class ActorCritic:
def __init__(self):
self.rand_generator = None
self.actor_step_size = None
self.critic_step_size = None
self.avg_reward_step_size = None
self.tc = None
self.avg_reward = None
self.critic_w = None
self.actor_w = None
self.softmax_prob = None
self.prev_tiles = None
self.last_action = None
def agent_init(self):
########
self.rand_generator = np.random.RandomState(1)
iht_size = 2048
self.num_tilings = 64
self.num_tiles = 64
self.tc = CartPoleTileCoder(iht_size=iht_size, num_tilings=self.num_tilings, num_tiles=self.num_tiles)
self.actor_step_size = 1e-1/self.num_tiles
self.critic_step_size = 1e-0/self.num_tiles
self.avg_reward_step_size = 1e-2/self.num_tiles
self.actions = [-10, -3, -1, -0.1, -0.01, 0, 0.01, 0.1, 1, 3, 10]
self.avg_reward = 0.0
self.actor_w = np.zeros((len(self.actions), iht_size))
self.critic_w = np.zeros(iht_size)
self.softmax_prob = None
self.prev_tiles = None
self.last_action = None
self.x = None
self.Q = np.matrix(
[[5, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 10, 0],
[0, 0, 0, 1],])
self.R = np.matrix([[0]])
def print_Agent(self):
print("Reward", self.reward, np.shape(self.reward))
print("Avg reward", self.avg_reward, np.shape(self.avg_reward))
print("x", self.x, np.shape(self.x))
print("Previous tiles", self.prev_tiles, np.shape(self.prev_tiles))
print("Softmax prob", self.softmax_prob, np.shape(self.softmax_prob))
print("Last action", self.last_action, np.shape(self.last_action))
print("Actor_w", self.actor_w[:, self.prev_tiles].sum(axis=1))
print("Critic_w", self.critic_w[self.prev_tiles].sum())
def _perception(self, observation):
self.x = np.matrix(observation.reshape((4,1)))
def agent_policy(self, active_tiles):
softmax_prob = compute_softmax_prob(self.actor_w, active_tiles)
chosen_action = self.rand_generator.choice(self.actions, p=softmax_prob)
self.softmax_prob = softmax_prob
return chosen_action
def _reward(self):
#self.reward = -(self.x.T @ self.Q @ self.x + np.multiply(self.last_action**2, self.R)).item() * 10
self.reward = -self.x[2,0].item()**2 - 0.2*self.x[0,0].item()**2 + 0.2
return self.reward
def agent_start(self, observation):
self._perception(observation)
active_tiles = self.tc.get_tiles(np.asarray(self.x)[:,0])
current_action = self.agent_policy(active_tiles)
self.last_action = current_action
self.prev_tiles = np.copy(active_tiles)
return self.last_action
def step(self, observation):
self._perception(observation)
active_tiles = self.tc.get_tiles(np.asarray(self.x)[:,0])
reward = self._reward()
#delta = reward + 0.99*self.critic_w[active_tiles].sum() - self.critic_w[self.prev_tiles].sum()
delta = reward - self.avg_reward + \
self.critic_w[active_tiles.reshape((1, self.num_tilings))].sum() - \
self.critic_w[self.prev_tiles.reshape((1, self.num_tilings))].sum()
self.avg_reward += self.avg_reward_step_size * delta
self.critic_w[self.prev_tiles.reshape((1,self.num_tilings))] += self.critic_step_size * delta
for i, a in enumerate(self.actions):
if a == self.last_action:
self.actor_w[i][self.prev_tiles.reshape((1,self.num_tilings))] += self.actor_step_size * delta * (1 - self.softmax_prob[i])
else:
self.actor_w[i][self.prev_tiles.reshape((1,self.num_tilings))] += self.actor_step_size * delta * (0 - self.softmax_prob[i])
current_action = self.agent_policy(active_tiles)
self.prev_tiles = active_tiles
self.last_action = current_action
#self.print_Agent()
self.actor_w = np.clip(self.actor_w, -10_000, 10_000)
self.critic_w = np.clip(self.critic_w, -10_000, 10_000)
return self.last_action
if __name__ == '__main__':
observation_0 = np.linspace(-2, 2, num = 3)
observation_1 = np.linspace(-4, 4, num = 3)
observation_2 = np.linspace(-np.pi/4, np.pi/4, num = 3)
observation_3 = np.linspace(-np.pi/2, np.pi/2, num = 3)
test_obs = list(itertools.product(observation_0, observation_1, observation_2, observation_3))
pctc = CartPoleTileCoder(iht_size=4096, num_tilings=8, num_tiles=2)
result = []
for obs in test_obs:
tiles = pctc.get_tiles(obs)
print(tiles)