This repository has been archived by the owner on Oct 27, 2020. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 52
/
run_goal_qpamdp.py
130 lines (114 loc) · 5.8 KB
/
run_goal_qpamdp.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
import click
import time
import gym
import os
import numpy as np
import gym_goal
from agents.qpamdp import QPAMDPAgent
from agents.sarsa_lambda import SarsaLambdaAgent
from common.wrappers import ScaledStateWrapper, QPAMDPScaledParameterisedActionWrapper
from gym_goal.envs.config import GOAL_WIDTH, PITCH_WIDTH, PITCH_LENGTH
from gym.wrappers import Monitor
from common.goal_domain import CustomFourierBasis, GoalObservationWrapper
variances = [0.01, 0.01, 0.01]
xfear = 50.0 / PITCH_LENGTH
yfear = 50.0 / PITCH_WIDTH
caution = 5.0 / PITCH_WIDTH
kickto_weights = np.array([[2.5, 1, 0, xfear, 0], [0, 0, 1 - caution, 0, yfear]])
initial_parameter_weights = [
kickto_weights,
np.array([[GOAL_WIDTH / 2 - 1, 0]]),
np.array([[-GOAL_WIDTH / 2 + 1, 0]])
]
def evaluate(env, agent, episodes=1000):
returns = []
timesteps = []
for _ in range(episodes):
state, _ = env.reset()
terminal = False
t = 0
total_reward = 0.
while not terminal:
t += 1
state = np.array(state, dtype=np.float32, copy=False)
action = agent.act(state)
(state, _), reward, terminal, _ = env.step(action)
total_reward += reward
timesteps.append(t)
returns.append(total_reward)
return np.array(returns)
@click.command()
@click.option('--seed', default=7, help='Random seed.', type=int)
@click.option('--episodes', default=20000, help='Number of epsiodes.', type=int)
@click.option('--evaluation-episodes', default=100, help='Episodes over which to evaluate after training.', type=int)
@click.option('--scale', default=False, help='Scale inputs and actions.', type=bool) # default 50, 25 best
@click.option('--initialise-params', default=True, help='Initialise action parameters.', type=bool)
@click.option('--save-dir', default="results/goal", help='Output directory.', type=str)
@click.option('--title', default="QPAMDP", help="Prefix of output files", type=str)
def run(seed, episodes, evaluation_episodes, scale, initialise_params, save_dir, title):
alpha_param = 0.1
env = gym.make('Goal-v0')
env = GoalObservationWrapper(env)
if scale:
variances[0] = 0.0001
variances[1] = 0.0001
variances[2] = 0.0001
alpha_param = 0.06
initial_parameter_weights[0] = np.array([[-0.375, 0.5, 0, 0.0625, 0],
[0, 0, 0.8333333333333333333, 0, 0.111111111111111111111111]])
initial_parameter_weights[1] = np.array([0.857346647646219686, 0])
initial_parameter_weights[2] = np.array([-0.857346647646219686, 0])
env = ScaledStateWrapper(env)
env = QPAMDPScaledParameterisedActionWrapper(env)
dir = os.path.join(save_dir, title)
env = Monitor(env, directory=os.path.join(dir, str(seed)), video_callable=False, write_upon_reset=False, force=True)
env.seed(seed)
np.random.seed(seed)
action_obs_index = np.arange(14)
param_obs_index = np.array([
np.array([10, 11, 14, 15]), # ball_features
np.array([16]), # keeper_features
np.array([16]), # keeper_features
])
basis = CustomFourierBasis(14, env.observation_space.spaces[0].low[:14], env.observation_space.spaces[0].high[:14])
discrete_agent = SarsaLambdaAgent(env.observation_space.spaces[0], env.action_space.spaces[0], basis=basis, seed=seed, alpha=0.01,
lmbda=0.1, gamma=0.9, temperature=1.0, cooling=1.0, scale_alpha=False,
use_softmax=True,
observation_index=action_obs_index, gamma_step_adjust=False)
agent = QPAMDPAgent(env.observation_space.spaces[0], env.action_space, alpha=alpha_param, initial_action_learning_episodes=4000,
seed=seed, action_obs_index=action_obs_index, parameter_obs_index=param_obs_index,
variances=variances, discrete_agent=discrete_agent, action_relearn_episodes=2000,
parameter_updates=1000, parameter_rollouts=50, norm_grad=True, print_freq=100,
phi0_func=lambda state: np.array([1, state[1], state[1]**2]),
phi0_size=3)
# Alternating learning periods from original paper:
# QPAMDP(1) : init(2000), parameter_updates(50), relearn(50)
# QPAMDP(infinity) : init(2000), parameter_updates(1000), relearn(2000)
# needed to increase initial action learning episodes to 4000
if initialise_params:
for a in range(3):
agent.parameter_weights[a] = initial_parameter_weights[a]
max_steps = 150
start_time = time.time()
agent.learn(env, episodes, max_steps)
end_time = time.time()
print("Training took %.2f seconds" % (end_time - start_time))
env.close()
returns = np.array(env.get_episode_rewards())
print("Saving training results to:",os.path.join(dir, "QPAMDP{}".format(str(seed))))
np.save(os.path.join(dir, title + "{}".format(str(seed))), returns)
print("Ave. return =", sum(returns) / len(returns))
print("Ave. last 100 episode return =", sum(returns[-100:]) / 100.)
print('Total P(S):{0:.4f}'.format((returns == 50.).sum() / len(returns)))
print('Ave. last 100 episode P(S):{0:.4f}'.format((returns[-100:] == 50.).sum() / 100.))
if evaluation_episodes > 0:
print("Evaluating agent over {} episodes".format(evaluation_episodes))
agent.variances = 0
agent.discrete_agent.epsilon = 0.
agent.discrete_agent.temperature = 0.
evaluation_returns = evaluate(env, agent, evaluation_episodes)
print("Ave. evaluation return =", sum(evaluation_returns) / len(evaluation_returns))
print("Ave. evaluation prob. =", sum(evaluation_returns == 50.) / len(evaluation_returns))
np.save(os.path.join(dir, title + "{}e".format(str(seed))), evaluation_returns)
if __name__ == '__main__':
run()