import gym
from DMP_Env_1D_static_MCTS import deep_mobile_printing_1d1r_MCTS
import matplotlib.pyplot as plt
import os
import uct
import numpy as np
import time
save_path = "./log/"
if os.path.exists(save_path) == False:
os.makedirs(save_path)
### Parameters
env = deep_mobile_printing_1d1r_MCTS(plan_choose=0)
agent = uct.UCT(action_space=env.action_space,
rollouts=2,
horizon=0,
ucb_constant=0.5,
is_model_dynamic=True)
### Run
# =============================================================================
# env.reset()
# done = False
#
# fig = plt.figure(figsize=(5, 5))
# ax = fig.add_subplot(1, 1, 1)
# print(env.total_brick)
# print(env.one_hot)
# ax.clear()
# Total_reward=0
# while True:
def next_move(self):
return uct.UCT(self.state, self.n_iter, self.verbose)
def forward(self, s, a):
x = torch.cat((s, a), dim=1)
x = self.fc_1(x)
x = self.relu(x)
x = self.fc_2(x)
x = self.relu(x)
x = self.fc_3(x)
x = self.relu(x)
Q = self.out(x)
return Q
UCT_mcts = uct.UCT(action_space=env.action_space,
rollouts=ROLLOUT,
horizon=100,
ucb_constant=UCB_CONSTANT,
is_model_dynamic=True)
class DQN_AGNET():
def __init__(self, device):
self.Eval_net = Q_NET().to(device)
self.Target_net = Q_NET().to(device)
self.device = device
self.learn_step = 0 # counting the number of learning for update traget periodiclly
self.count_memory = 0 # counting the transitions
# self.replay_memory = np.zeros((Replay_memory_size, State_dim * 2 + 2))
self.replay_memory = deque(maxlen=Replay_memory_size)
self.optimizer = torch.optim.Adam(self.Eval_net.parameters(), lr=Lr)
self.greedy_epsilon = 0.2
self.relu = nn.ReLU()
def forward(self, s,a):
x=torch.cat((s,a),dim=1)
x = self.fc_1(x)
x = self.relu(x)
x = self.fc_2(x)
x = self.relu(x)
x = self.fc_3(x)
x = self.relu(x)
Q = self.out(x)
return Q
UCT_mcts = uct.UCT(
action_space=env.action_space,
rollouts=20,
horizon=100,
ucb_constant=0.5,
is_model_dynamic=True
)
class DQN_AGNET():
def __init__(self,device):
self.Eval_net= Q_NET().to(device)
self.Target_net = Q_NET().to(device)
self.device=device
self.learn_step = 0 # counting the number of learning for update traget periodiclly
self.count_memory = 0 # counting the transitions
# self.replay_memory = np.zeros((Replay_memory_size, State_dim * 2 + 2))
self.replay_memory = deque(maxlen=Replay_memory_size)
self.optimizer = torch.optim.Adam(self.Eval_net.parameters(), lr=Lr)
self.greedy_epsilon=0.2