-
Notifications
You must be signed in to change notification settings - Fork 0
/
watch_simulation.py
77 lines (58 loc) · 1.91 KB
/
watch_simulation.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
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import argparse
import gym
from config import Config
from agents.double_dqn_agent import DoubleDQNAgent
from agents.ddpg_agent import DDPGAgent
from memory import Memory
from utils import plot_final_results
def get_environment(mode="discrete"):
if mode == "discrete":
return gym.make("LunarLander-v2")
else:
return gym.make("LunarLanderContinuous-v2")
def get_agent(device, env, mode="discrete"):
if mode == "discrete":
agent = DoubleDQNAgent(device, 0, env)
agent.load_model("./saved_models/model_double_dqn.pt")
return agent
else:
agent = DDPGAgent(device, 0, env)
agent.load_model("./saved_models/model_ddpg.pt")
return agent
def run_simulation(max_steps, num_episodes = 1, mode="discrete"):
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
env = get_environment(mode)
agent = get_agent(device, env, mode)
with torch.no_grad():
for episode in range(num_episodes):
state = env.reset()
episode_reward = 0
for step in range(max_steps):
action = agent.get_action(state, 0)
env.render()
next_state, reward, done, _ = env.step(action)
episode_reward += reward
state = next_state
if done:
break
# End of episode
# episode_rewards.append(episode_reward)
return
def main(args):
run_simulation(
max_steps=args.max_steps,
num_episodes=args.episodes,
mode=args.mode
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-ep", "--episodes", type=int, default=100, help="number of episodes")
parser.add_argument("--max-steps", type=int, default=1000, help="max steps for episode")
parser.add_argument("--mode", type=str, default="discrete", help="environment mode")
args = parser.parse_args()
main(args)