class DQNLunar:
def __init__(self):
self.env = gym.make('LunarLander-v2')
self.nn = DQN(8, 4)
self.avgReward = np.zeros(100)
def runEp(self, env, printT=False):
state = env.reset()
state = np.reshape(state, [1, 8])
totalreward = 0
for _ in range(500):
if printT:
env.render()
action = self.nn.chooseAction(state)
next_state, reward, done, _ = env.step(action)
totalreward += reward
next_state = np.reshape(next_state, [1, 8])
self.nn.storePastResults(state, action, reward, next_state, done)
state = next_state
if done:
break
self.nn.train(6)
return totalreward
def run(self):
for i in range(20000): # Number of episodes s
if i % 100 == 0:
self.avgReward[i % 100] = self.runEp(self.env, True)
avg = np.average(self.avgReward)
print('Episode %f/10000 Reward: %f' % (i, avg))
else:
self.avgReward[i % 100] = self.runEp(self.env, True)
print(i, self.avgReward[i % 100])
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
######################################################################
init_screen = get_screen()
_, _, screen_height, screen_width = init_screen.shape
# Get number of actions from gym action space
n_actions = 4
policy_net = DQN(screen_height, screen_width, n_actions,layers=20).to(device)
target_net = DQN(screen_height, screen_width, n_actions,layers=20).to(device)
PATH = 'C:/Users/sagau/Google Drive/smaller1.pth'
optimizer = optim.Adam(policy_net.parameters(),lr=1e-4)
load_mode = False
if load_mode:
model_dict = torch.load(PATH,map_location=torch.device('cpu'))
i_episode = model_dict['epoch']
optimizer.load_state_dict(model_dict['optimizer'])
policy_net.load_state_dict(model_dict['state_dict'])
target_net.load_state_dict(model_dict['state_dict'])
episode_durations = model_dict['episode_durations']
total_reward_list = model_dict['total_reward_list']
point_list = model_dict['point_list']
plot_durations()
return torch.tensor([[random.randrange(n_actions)]], device=device, dtype=torch.long)
######################################################################
# Training loop
######################################################################
init_screen = get_screen()
_, _, screen_height, screen_width = init_screen.shape
# Get number of actions from gym action space
n_actions = 4
#PATH = 'C:/Users/sagau/Desktop/Kaggle/TetrisRepo/models/model1_2.pth'
policy_net = DQN(screen_height, screen_width, n_actions,layers=20)
policy_net.eval()
policy_net = DQN(screen_height, screen_width, n_actions,layers=20).to(device)
PATH = 'C:/Users/sagau/Google Drive/transfersmaller.pth'
model_dict = torch.load(PATH,map_location=torch.device('cpu'))
policy_net.load_state_dict(model_dict['state_dict'])
######################################################################
# Play with model !
sleep_time = 0.2
game = Tetris(nb_rows=8,nb_cols=6)
done = game.generate_block(choice=random.randint(0,3))
rows = 0
for t in count():
# for t in range(200):
import gym
import numpy as np
from dqnClass import DQN
# Setup Enviornment
env = gym.make('CartPole-v0')
nn = DQN(4, 2)
def runEp(env, printT=False):
state = env.reset()
state = np.reshape(state, [1, 4])
totalreward = 0
for t in range(500):
if printT:
env.render()
action = nn.chooseAction(state)
next_state, reward, done, _ = env.step(action)
totalreward += reward
next_state = np.reshape(next_state, [1, 4])
nn.storePastResults(state, action, reward, next_state, done)
state = next_state
if done:
break
nn.train(32)
return t
avgReward = np.zeros(200)
for i in range(10000): # Number of episodes s
score = runEp(env, True)
import gym
import numpy as np
from dqnClass import DQN
# Setup Enviornment
env = gym.make('LunarLander-v2')
nn = DQN(8, 4)
def runEp(env, printT=False):
state = env.reset()
state = np.reshape(state, [1, 8])
totalreward = 0
for _ in range(500):
if printT:
env.render()
action = nn.chooseAction(state)
next_state, reward, done, _ = env.step(action)
totalreward += reward
next_state = np.reshape(next_state, [1, 8])
nn.storePastResults(state, action, reward, next_state, done)
state = next_state
if done:
break
nn.train(6)
return totalreward
avgReward = np.zeros(100)
for i in range(20000): # Number of episodes s
if i % 100 == 0:
def __init__(self):
self.env = gym.make('LunarLander-v2')
self.nn = DQN(8, 4)
self.avgReward = np.zeros(100)