def main():
env = gym.make('CartPole-v0').unwrapped
batch_size = 128
gamma = 0.999
eps_start = 0.9
eps_end = 0.05
eps_decay = 200
target_update = 10
n_actions = env.action_space.n
env.reset()
init_screen = get_screen(env)
_, _, screen_height, screen_width = init_screen.shape
episode_durations = []
agent = Agent(gamma=gamma,
n_actions=n_actions,
screen_height=screen_height,
screen_width=screen_width,
batch_size=batch_size)
n_games = 500
scores = []
eps_history = []
for i in range(n_games):
done = False
length = 0
state = env.reset()
last_screen = get_screen(env)
current_screen = get_screen(env)
state = current_screen - last_screen
# env.render()
while not done:
action = agent.select_action(state)
_, reward, done, _ = env.step(action.item())
last_screen = current_screen
current_screen = get_screen(env)
next_state = current_screen - last_screen if not done else None
agent.store_transition(state, action, next_state, reward)
state = next_state
agent.learn()
length += 1
episode_durations.append(length)
# plot_durations()
if i % target_update == 0:
agent.update_target()
#imagine pretty plots
print('done')
env.close()
def main():
global args
args = parser.parse_args()
env = make(game='SonicTheHedgehog-Genesis', state='LabyrinthZone.Act1')
current_state = get_screen(env)
for epoch in range(args.epochs):
env.reset()
def test(self, env, n_epochs=30, verbose=False):
rewards = []
self.policy_net = self.policy_net.cuda()
self.target_net = self.target_net.cuda()
self.target_net.eval()
for epoch in range(n_epochs):
env.reset()
done = False
epoch_rewards = []
video = []
last_screen = get_screen(env)
current_screen = get_screen(env)
state = current_screen - last_screen
while not done:
if epoch % 5 == 0:
video.append(last_screen)
action = self.select_action(state, 0.)
_, reward, done, _ = env.step(action[0, 0])
last_screen = current_screen
current_screen = get_screen(env)
if not done:
next_state = current_screen - last_screen
else:
next_state = None
epoch_rewards.append(reward)
reward = Tensor([reward])
state = next_state
logging.debug(
'Test epoch {} : reward= {}, duration= {}'.format(
epoch, np.sum(epoch_rewards), len(epoch_rewards)))
rewards.append(np.sum(epoch_rewards))
if epoch % 5 == 0:
self.make_video(video, ext='_test_' + str(epoch))
logging.info('Performance estimate : {} pm {}'.format(
np.mean(rewards), np.std(rewards)))
def trainDQN(file_name="DQN",
env=GridworldEnv(1),
batch_size=128,
gamma=0.999,
eps_start=0.9,
eps_end=0.05,
eps_decay=1000,
is_plot=False,
num_episodes=500,
max_num_steps_per_episode=1000,
learning_rate=0.0001,
memory_replay_size=10000):
"""
DQN training routine. Retuns rewards and durations logs.
Plot environment screen
"""
if is_plot:
env.reset()
plt.ion()
plt.figure()
plt.imshow(get_screen(env).cpu().squeeze(0).squeeze(0).numpy(),
interpolation='none')
plt.title("")
plt.draw()
plt.pause(0.00001)
num_actions = env.action_space.n
model = DQN(num_actions)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
use_cuda = torch.cuda.is_available()
if use_cuda:
model.cuda()
memory = ReplayMemory(memory_replay_size)
episode_durations = []
mean_durations = []
episode_rewards = []
mean_rewards = []
steps_done = 0 # total steps
for i_episode in range(num_episodes):
if i_episode % 20 == 0:
clear_output()
print("Cur episode:", i_episode, "steps done:", steps_done,
"exploration factor:", eps_end + (eps_start - eps_end) * \
math.exp(-1. * steps_done / eps_decay))
# Initialize the environment and state
env.reset()
# last_screen = env.current_grid_map
# (1, 1, 8, 8)
current_screen = get_screen(env)
state = current_screen # - last_screen
for t in count():
# Select and perform an action
action = select_action(state, model, num_actions, eps_start,
eps_end, eps_decay, steps_done)
steps_done += 1
_, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# Observe new state
last_screen = current_screen
current_screen = get_screen(env)
if not done:
next_state = current_screen # - last_screen
else:
next_state = None
# Store the transition in memory
memory.push(state, action, next_state, reward)
# Move to the next state
state = next_state
# plot_state(state)
# env.render()
# Perform one step of the optimization (on the target network)
optimize_model(model, optimizer, memory, batch_size, gamma)
if done or t + 1 >= max_num_steps_per_episode:
episode_durations.append(t + 1)
episode_rewards.append(env.episode_total_reward)
if is_plot:
plot_durations(episode_durations, mean_durations)
plot_rewards(episode_rewards, mean_rewards)
break
print('Complete')
env.render(close=True)
env.close()
if is_plot:
plt.ioff()
plt.show()
## Store Results
np.save(file_name + '-dqn-rewards', episode_rewards)
np.save(file_name + '-dqn-durations', episode_durations)
return model, episode_rewards, episode_durations
def trainD(file_name="Distral_1col",
list_of_envs=[GridworldEnv(4), GridworldEnv(5)],
batch_size=128,
gamma=0.999,
alpha=0.9,
beta=5,
eps_start=0.9,
eps_end=0.05,
eps_decay=5,
is_plot=False,
num_episodes=200,
max_num_steps_per_episode=1000,
learning_rate=0.001,
memory_replay_size=10000,
memory_policy_size=1000):
"""
Soft Q-learning training routine. Retuns rewards and durations logs.
Plot environment screen
"""
num_actions = list_of_envs[0].action_space.n
num_envs = len(list_of_envs)
policy = PolicyNetwork(num_actions)
models = [DQN(num_actions)
for _ in range(0, num_envs)] ### Add torch.nn.ModuleList (?)
memories = [
ReplayMemory(memory_replay_size, memory_policy_size)
for _ in range(0, num_envs)
]
use_cuda = torch.cuda.is_available()
if use_cuda:
policy.cuda()
for model in models:
model.cuda()
optimizers = [
optim.Adam(model.parameters(), lr=learning_rate) for model in models
]
policy_optimizer = optim.Adam(policy.parameters(), lr=learning_rate)
# optimizer = optim.RMSprop(model.parameters(), )
episode_durations = [[] for _ in range(num_envs)]
episode_rewards = [[] for _ in range(num_envs)]
steps_done = np.zeros(num_envs)
episodes_done = np.zeros(num_envs)
current_time = np.zeros(num_envs)
# Initialize environments
for env in list_of_envs:
env.reset()
while np.min(episodes_done) < num_episodes:
# TODO: add max_num_steps_per_episode
# Optimization is given by alterating minimization scheme:
# 1. do the step for each env
# 2. do one optimization step for each env using "soft-q-learning".
# 3. do one optimization step for the policy
for i_env, env in enumerate(list_of_envs):
# print("Cur episode:", i_episode, "steps done:", steps_done,
# "exploration factor:", eps_end + (eps_start - eps_end) * \
# math.exp(-1. * steps_done / eps_decay))
# last_screen = env.current_grid_map
current_screen = get_screen(env)
state = current_screen # - last_screen
# Select and perform an action
action = select_action(state, policy, models[i_env], num_actions,
eps_start, eps_end, eps_decay,
episodes_done[i_env], alpha, beta)
steps_done[i_env] += 1
current_time[i_env] += 1
_, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# Observe new state
last_screen = current_screen
current_screen = get_screen(env)
if not done:
next_state = current_screen # - last_screen
else:
next_state = None
# Store the transition in memory
time = Tensor([current_time[i_env]])
memories[i_env].push(state, action, next_state, reward, time)
# Perform one step of the optimization (on the target network)
optimize_model(policy, models[i_env], optimizers[i_env],
memories[i_env], batch_size, alpha, beta, gamma)
if done:
print(
"ENV:", i_env, "iter:", episodes_done[i_env], "\treward:",
env.episode_total_reward, "\tit:", current_time[i_env],
"\texp_factor:", eps_end + (eps_start - eps_end) *
math.exp(-1. * episodes_done[i_env] / eps_decay))
env.reset()
episodes_done[i_env] += 1
episode_durations[i_env].append(current_time[i_env])
current_time[i_env] = 0
episode_rewards[i_env].append(env.episode_total_reward)
if is_plot:
plot_rewards(episode_rewards, i_env)
optimize_policy(policy, policy_optimizer, memories, batch_size,
num_envs, gamma)
print('Complete')
env.render(close=True)
env.close()
if is_plot:
plt.ioff()
plt.show()
## Store Results
np.save(file_name + '-distral-2col-rewards', episode_rewards)
np.save(file_name + '-distral-2col-durations', episode_durations)
return models, policy, episode_rewards, episode_durations
def main():
#gettign positions of rigib bodies in real time
client = NatClient()
arena_rb = client.rigid_bodies['Arena']
rat_rb = client.rigid_bodies['Rat']
window = pyglet.window.Window(resizable=True, fullscreen=True, screen=get_screen(1)) # Opening the basic pyglet window
# Load Arena
remove_image_lines_from_mtl('assets/3D/grass_scene.mtl')
arena_filename = 'assets/3D/grass_scene.obj'# we are taking an arena which has been opened in blender and rendered to 3D after scanning it does not have flipped normals
arena_reader = rc.WavefrontReader(arena_filename) # loading the mesh of the arena thought a wavefrontreader
arena = arena_reader.get_mesh("Arena", position=arena_rb.position) # making the wafrotn into mesh so we can extrude texture ont top of it.
arena.uniforms['diffuse'] = 1., 1., 1. # addign a white diffuse material to the arena
arena.rotation = arena.rotation.to_quaternion() # we also need to get arena's rotation not just xyz so it can be tracked and moved if it gets bumped
# Load the projector as a Ratcave camera, set light to its position
projector = rc.Camera.from_pickle('assets/3D/projector.pkl') # settign the pickle filled of the projector, which gives us the coordinates of where the projector is
projector.position.x += .004
projector.projection = rc.PerspectiveProjection(fov_y =40.5, aspect=1.777777778)
light = rc.Light(position=projector.position)
## Make Virtual Scene ##
fields = []
for x, z in itertools.product([-.8, 0, .8], [-1.6, 0, 1.6]):
field = load_textured_mesh(arena_reader, 'grass', 'grass.png')
field.position.x += x
field.position.z += z
fields.append(field)
ground = load_textured_mesh(arena_reader, 'Ground', 'dirt.png')
sky = load_textured_mesh(arena_reader, 'Sky', 'sky.png')
snake = load_textured_mesh(arena_reader, 'Snake', 'snake.png')
rat_camera = rc.Camera(projection=rc.PerspectiveProjection(aspect=1, fov_y=90, z_near=.001, z_far=10), position=rat_rb.position) # settign the camera to be on top of the rats head
meshes = [ground, sky, snake] + fields
for mesh in meshes:
mesh.uniforms['diffuse'] = 1., 1., 1.
mesh.uniforms['flat_shading'] = False
mesh.parent = arena
virtual_scene = rc.Scene(meshes=meshes, light=light, camera=rat_camera, bgColor=(0, 0, 255)) # seetign aset virtual scene to be projected as the mesh of the arena
virtual_scene.gl_states.states = virtual_scene.gl_states.states[:-1]
## Make Cubemapping work on arena
cube_texture = rc.TextureCube(width=4096, height=4096) # usign cube mapping to import eh image on the texture of the arena
framebuffer = rc.FBO(texture=cube_texture) ## creating a fr`amebuffer as the texture - in tut 4 it was the blue screen
arena.textures.append(cube_texture)
# Stereo
vr_camgroup = rc.StereoCameraGroup(distance=.05)
vr_camgroup.rotation = vr_camgroup.rotation.to_quaternion()
# updating the posiotn of the arena in xyz and also in rotational perspective
def update(dt):
"""main update function: put any movement or tracking steps in here, because it will be run constantly!"""
vr_camgroup.position, vr_camgroup.rotation.xyzw = rat_rb.position, rat_rb.quaternion # setting the actual osiont of the rat camera to vbe of the rat position
arena.uniforms['playerPos'] = rat_rb.position
arena.position, arena.rotation.xyzw = arena_rb.position, arena_rb.quaternion
arena.position.y -= .02
pyglet.clock.schedule(update) # making it so that the app updates in real time
@window.event
def on_draw():
## Render virtual scene onto cube texture
with framebuffer:
with cube_shader:
for mask, camside in zip([(True, False, False, True), (False, True, True, True)], [vr_camgroup.left, vr_camgroup.right]):
gl.glColorMask(*mask)
virtual_scene.camera.position.xyz = camside.position_global
virtual_scene.draw360_to_texture(cube_texture)
## Render real scene onto screen
gl.glColorMask(True, True, True, True)
window.clear()
with cube_shader: # usign cube shader to create the actuall 6 sided virtual cube which gets upated with position and angle of the camera/viewer
rc.clear_color(255, 0, 0)
# why is it here 39? e
with projector, light:
arena.draw()
# actually run everything.
pyglet.app.run()
def train(self,
env,
n_epochs=30,
epsilon_init=1.,
epsilon_schedule='exp',
eps_decay=None,
lr=0.001,
batch_size=32):
if epsilon_schedule == 'linear':
eps_range = np.linspace(epsilon_init, 0., n_epochs)
elif epsilon_schedule == 'constant':
eps_range = [epsilon_init for _ in range(n_epochs)]
elif epsilon_schedule == 'exp':
if not eps_decay:
eps_decay = n_epochs // 4
eps_range = [
epsilon_init * math.exp(-1. * i / eps_decay)
for i in range(n_epochs)
]
history_file = open(self.filename + 'history', mode='a+')
self.policy_net = self.policy_net.cuda()
self.target_net = self.target_net.cuda()
self.target_net.eval()
self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=lr)
losses, rewards, change_history = [], [], []
for epoch in range(n_epochs):
env.reset()
last_screen = get_screen(env)
current_screen = get_screen(env)
state = current_screen - last_screen
done = False
epoch_losses = []
epoch_rewards = []
video = []
while not done:
if epoch % 10 == 1:
video.append(last_screen)
action = self.select_action(state, eps_range[epoch])
_, reward, done, _ = env.step(action[0, 0])
last_screen = current_screen
current_screen = get_screen(env)
reward = Tensor([reward])
if not done:
next_state = current_screen - last_screen
else:
next_state = None
self.memory.push(state, action, next_state, reward)
state = next_state
loss = self.update(batch_size=batch_size)
epoch_losses.append(loss)
epoch_rewards.append(reward)
history_file.write(
'Epoch {}: loss= {}, reward= {}, duration= {}\n'.format(
epoch, np.mean(epoch_losses), np.sum(epoch_rewards),
len(epoch_rewards)))
losses.append(np.mean(epoch_losses))
rewards.append(np.sum(epoch_rewards))
if epoch % 10 == 1:
self.target_net.load_state_dict(self.policy_net.state_dict())
self.save(ext=str(epoch))
self.make_video(video, ext='_train_' + str(epoch))
with open(self.filename + '.train_losses', 'a+') as f:
for l in losses:
f.write(str(l) + '\n')
losses = []
with open(self.filename + '.train_rewards', 'a+') as f:
for r in rewards:
f.write(str(r) + '\n')
rewards = []
self.save()
def train(Learner):
# global args
# args = parser.parse_args()
# Learner = DQN().to(device)
# env = retro.make(game='Airstriker-Genesis', state='Level1')
criterion = L2_loss(0.99).to(device)
if use_cuda:
Learner = Learner.cuda()
criterion = criterion.cuda()
optimizer = optim.SGD(Learner.parameters(), lr=0.01)
eps_threshold = 0.3
RM = ReplayMemory(900)
A_agent = ActorAgent(Learner, args)
print("Start Episodes")
for i_episode in range(50000):
env.reset()
A_agent.reset(Learner, args)
last_state = get_screen(env)
current_state = get_screen(env)
state = current_state - last_state
# state_var = torch.autograd.Variable(state)
state_var = state.to(device)
total_reward = 0
# if i_episode % 50 == 0:
# if not eps_threshold < 0.1:
# eps_threshold -= 0.001
start = time.time()
for t in count():
if t == 0:
print("episode begin")
action_q = A_agent.act(state_var, eps_threshold)
"""
if is_cuda:
action_q = action_q.cpu()
_, action = action_q.data.max(2)
else:
_, action = action_q.data.max(2)
"""
_, action = action_q.data.max(2)
action_numpy = action.squeeze(0).numpy()
# print(list(action_numpy))
for i in range(4):
_, reward, done, _ = env.step(action_numpy)
total_reward += reward
last_state = current_state
current_state = get_screen(env)
state = current_state - last_state
# state_var = torch.autograd.Variable(state)
state_var = state.to(device)
# 行動語のstateを保存
A_agent.add_to_buffer(reward, action_q, state_var)
# ReplayMemoryに状態保存
if len(A_agent.localbuffer) > 10:
p, error = calc_priority_TDerror(Learner, criterion, A_agent,
10)
RM.push(p, error)
if done:
break
if t == 500:
print("Total time: {0:.2f}".format(time.time() - start))
# break
# Optimize Learner model
# if t%100==0 and len(A_agent.localbuffer)>80 and len(RM)>=30:
env.render()
# update Learner part
for i in range(4):
if len(RM.memory) >= 30:
error_batch = RM.priority_sample(30)
optimizer.zero_grad()
# error_batch.backward(retain_graph=True)
error_batch.backward()
optimizer.step()
# for param in Learner.parameters():
# param.grad.data.clamp_(-1, 1)
optimizer.step()
print("{0}\t{1}\tLoss:{2:.2f}\tTotal:{3:.2f}\tReward:{4:.2f}".
format(
i_episode,
t,
float(error_batch),
total_reward,
reward,
))
else:
break
if i_episode % 5 == 0:
env.reset()
last_state = get_screen(env)
current_state = get_screen(env)
state = current_state - last_state
state_var = state.to(device)
val_reward = 0
for t in count():
with torch.no_grad():
action_q = Learner(state_var)
_, action = action_q.data.max(2)
action_numpy = action.squeeze(0).numpy()
for i in range(4):
_, reward, done, _ = env.step(action_numpy)
val_reward += reward
last_state = current_state
current_state = get_screen(env)
state = current_state - last_state
state_var = state.to(device)
if done:
break
env.render()
print("Validation:\tepisode{0}\tReward: {1:.2f}".format(
i_episode, val_reward))
with open("result.txt", "a") as f:
f.write("episode{0}\tReward: {1:.2f}".format(
i_episode, val_reward))
f.write("\n")
RM.reset()
# break
with open("total_reward.txt", "a") as f:
f.write("{0}\t{1}".format(i_episode, total_reward))
f.write("\n")
EPS_START = 0.9
EPS_END = 0.05
EPS_DECAY = 200
TARGET_UPDATE = 10
num_episoeds = 50
resize = T.Compose([
T.ToTensor(),
T.ToPILImage(),
T.Resize(40, interpolation=Image.CUBIC),
T.ToTensor()
])
if __name__ == "__main__":
env = gym.make(env_name)
init_screen = get_screen(env)
screen_height, screen_width, _ = init_screen.shape
# Get number of actions from gym action space
n_actions = env.action_space.n
policy_net = DQN(screen_height, screen_width, n_actions).to(device)
target_net = DQN(screen_height, screen_width, n_actions).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy_net.parameters())
memory = ReplayMemory(10000)
steps_done = 0
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env.reset()
BATCH_SIZE = 128
# GAMMA is the discount factor
GAMMA = 0.999
EPS_START = 0.9
EPS_END = 0.05
EPS_DECAY = 200
TARGET_UPDATE = 10
AVERAGE_SIZE = 10
episode_durations = []
init_screen = get_screen(env, device)
_, _, screen_height, screen_width = init_screen.shape
policy_net = DQN(screen_height, screen_width).to(device)
target_net = DQN(screen_height, screen_width).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy_net.parameters())
memory = ReplayMemory(10000)
steps_done = 0
num_episodes = 300
for i_episode in range(num_episodes):
env.reset()
from collections import deque
import gym
import numpy as np
from agent import DDPG
from utils import get_screen
env = gym.make('Pendulum-v0')
agent = DDPG(env, memory=False)
agent.load_model()
env.reset()
pixel = env.render(mode='rgb_array')
state = deque([get_screen(pixel) for _ in range(3)], maxlen=3)
cumulative_reward = 0
for timestep in range(200):
action = agent.get_action(np.array(state)[np.newaxis])
_, reward, _, _ = env.step(action * 2)
pixel = env.render(mode='rgb_array')
state_ = state.copy()
state_.append(get_screen(pixel))
state = state_
cumulative_reward += reward
print('Cumulative Reward: {}'.format(cumulative_reward))
def trainD(file_name="Distral_1col", list_of_envs=[GridworldEnv(4),
GridworldEnv(5)], batch_size=128, gamma=0.999, alpha=0.9,
beta=5, eps_start=0.9, eps_end=0.05, eps_decay=5,
is_plot=False, num_episodes=200,
max_num_steps_per_episode=1000, learning_rate=0.001,
memory_replay_size=10000, memory_policy_size=1000):
"""
Soft Q-learning training routine. Retuns rewards and durations logs.
Plot environment screen
"""
# action dimension
num_actions = list_of_envs[0].action_space.n
# total envs
num_envs = len(list_of_envs)
# pi_0
policy = PolicyNetwork(num_actions)
# Q value, every environment has one, used to calculate A_i,
models = [DQN(num_actions) for _ in range(0, num_envs)] ### Add torch.nn.ModuleList (?)
# replay buffer for env ???
memories = [ReplayMemory(memory_replay_size, memory_policy_size) for _ in range(0, num_envs)]
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# device = "cpu"
print(device)
# model
policy = policy.to(device)
for i in range(len(models)):
models[i] = models[i].to(device)
# optimizer for every Q model
optimizers = [optim.Adam(model.parameters(), lr=learning_rate)
for model in models]
# optimizer for policy
policy_optimizer = optim.Adam(policy.parameters(), lr=learning_rate)
# optimizer = optim.RMSprop(model.parameters(), )
# info list for each environment
episode_durations = [[] for _ in range(num_envs)] # list of local steps
episode_rewards = [[] for _ in range(num_envs)] # list of list of episode reward
episodes_done = np.zeros(num_envs) # episode num
steps_done = np.zeros(num_envs) # global timesteps for each env
current_time = np.zeros(num_envs) # local timesteps for each env
# Initialize environments
for env in list_of_envs:
env.reset()
while np.min(episodes_done) < num_episodes:
policy.train()
for model in models:
model.train()
# TODO: add max_num_steps_per_episode
# Optimization is given by alterating minimization scheme:
# 1. do the step for each env
# 2. do one optimization step for each env using "soft-q-learning".
# 3. do one optimization step for the policy
# 1. do the step for each env
for i_env, env in enumerate(list_of_envs):
# print("Cur episode:", i_episode, "steps done:", steps_done,
# "exploration factor:", eps_end + (eps_start - eps_end) * \
# math.exp(-1. * steps_done / eps_decay))
# last_screen = env.current_grid_map
# ===========update step info begin========================
current_screen = get_screen(env)
# state
state = current_screen # - last_screen
# action chosen by pi_1~pi_i
action = select_action(state, policy, models[i_env], num_actions,
eps_start, eps_end, eps_decay,
episodes_done[i_env], alpha, beta, device)
# global_steps
steps_done[i_env] += 1
# local steps
current_time[i_env] += 1
# reward
_, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# next state
last_screen = current_screen
current_screen = get_screen(env)
if not done:
next_state = current_screen # - last_screen
else:
next_state = None
# add to buffer
time = Tensor([current_time[i_env]])
memories[i_env].push(state, action, next_state, reward, time)
# 2. do one optimization step for each env using "soft-q-learning".
# Perform one step of the optimization (on the target network)
optimize_model(policy, models[i_env], optimizers[i_env],
memories[i_env], batch_size, alpha, beta, gamma, device)
# ===========update step info end ========================
# ===========update episode info begin ====================
if done:
print("ENV:", i_env, "iter:", episodes_done[i_env],
"\treward:", env.episode_total_reward,
"\tit:", current_time[i_env], "\texp_factor:", eps_end +
(eps_start - eps_end) * math.exp(-1. * episodes_done[i_env] / eps_decay))
# reset env
env.reset()
# episode steps
episodes_done[i_env] += 1
# append each episode local timesteps list for every env
episode_durations[i_env].append(current_time[i_env])
# reset local timesteps
current_time[i_env] = 0
# append total episode_reward to list
episode_rewards[i_env].append(env.episode_total_reward)
if is_plot:
plot_rewards(episode_rewards, i_env)
# ===========update episode info end ====================
# 3. do one optimization step for the policy
# after all envs has performed one step, optimize policy
optimize_policy(policy, policy_optimizer, memories, batch_size,
num_envs, gamma, device)
print('Complete')
env.render(close=True)
env.close()
if is_plot:
plt.ioff()
plt.show()
## Store Results
np.save(file_name + '-distral-2col-rewards', episode_rewards)
np.save(file_name + '-distral-2col-durations', episode_durations)
return models, policy, episode_rewards, episode_durations
import torch
import gym
from utils import get_screen
# Import environment
env = gym.make('Acrobot-v1')
# Define model and load pre-trained weights
trained_Q_network = torch.load('trained_DQN_model',
map_location=lambda storage, loc: storage)
# Reset env
_ = env.reset()
current_screen = get_screen(env)
state = current_screen
# Present trained behaviour over episodes
num_test_episodes = 30
episodes_passed = 0
acc_episodic_reward = 0.0
while episodes_passed < num_test_episodes:
# Choose action greedily
action = trained_Q_network.select_action(state)
# Act on env
_, reward, done, _ = env.step(action)
last_screen = current_screen
current_screen = get_screen(env)
next_state = current_screen - last_screen
# Add to accumulative reward
acc_episodic_reward += reward
# When episode is done - reset and print
def trainSQL0(file_name="SQL0", env=GridworldEnv(1), batch_size=128,
gamma=0.999, beta=5, eps_start=0.9, eps_end=0.05, eps_decay=1000,
is_plot=False, num_episodes=500, max_num_steps_per_episode=1000,
learning_rate=0.001, memory_replay_size=10000):
"""
Soft Q-learning training routine when observation vector is input
Retuns rewards and durations logs.
Plot environment screen
"""
if is_plot:
env.reset()
plt.ion()
plt.figure()
plt.imshow(get_screen(env).cpu().squeeze(0).squeeze(0).numpy(),
interpolation='none')
plt.draw()
plt.pause(0.00001)
num_actions = env.action_space.n
input_size = env.observation_space.shape[0]
model = DQN(input_size, num_actions)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# optimizer = optim.RMSprop(model.parameters(), )
use_cuda = torch.cuda.is_available()
if use_cuda:
model.cuda()
memory = ReplayMemory(memory_replay_size)
episode_durations = []
mean_durations = []
episode_rewards = []
mean_rewards = []
steps_done, t = 0, 0
# plt.ion()
for i_episode in range(num_episodes):
if i_episode % 20 == 0:
clear_output()
if i_episode != 0:
print("Cur episode:", i_episode, "steps done:", episode_durations[-1],
"exploration factor:", eps_end + (eps_start - eps_end) * \
math.exp(-1. * steps_done / eps_decay), "reward:", env.episode_total_reward)
# Initialize the environment and state
state = torch.from_numpy( env.reset() ).type(torch.FloatTensor).view(-1,input_size)
for t in count():
# Select and perform an action
action = select_action(state, model, num_actions,
eps_start, eps_end, eps_decay, steps_done)
next_state_tmp, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# Observe new state
next_state = torch.from_numpy( next_state_tmp ).type(torch.FloatTensor).view(-1,input_size)
if done:
next_state = None
# Store the transition in memory
memory.push(state, action, next_state, reward)
# Move to the next state
state = next_state
# plot_state(state)
# env.render()
# Perform one step of the optimization (on the target network)
optimize_model(model, optimizer, memory, batch_size, gamma, beta) #### Difference w.r.t DQN
if done or t + 1 >= max_num_steps_per_episode:
episode_durations.append(t + 1)
episode_rewards.append(env.episode_total_reward) ##### Modify for OpenAI envs such as CartPole
if is_plot:
plot_durations(episode_durations, mean_durations)
plot_rewards(episode_rewards, mean_rewards)
steps_done += 1
break
print('Complete')
env.render(close=True)
env.close()
if is_plot:
plt.ioff()
plt.show()
## Store Results
np.save(file_name + '-sql0-rewards', episode_rewards)
np.save(file_name + '-sql0-durations', episode_durations)
return model, episode_rewards, episode_durations
