def evaluate(actor_main, env, control_stepsize, state_dim, action_dim):
timestep = env.reset()
_, _, _, s = timestep
s = torch.FloatTensor(utils.state_1d_flat(s)).to(device)
action_dim = actor_main.action_dim
step_i = 0
ep_reward = 0
prev_action = np.zeros([action_dim])
while step_i < 1000:
with torch.no_grad():
a = actor_main.forward(s.view(-1, state_dim)).cpu().numpy()[0]
for _ in range(control_stepsize):
timestep = env.step(np.reshape(prev_action, (action_dim, )))
step_i += 1
if step_i > 1000:
break
if step_i > 1000:
break
t, r, _, s2 = timestep
s2 = torch.FloatTensor(utils.state_1d_flat(s2)).to(device)
s = s2
ep_reward += r * control_stepsize # normalize episode reward approximately 1000
prev_action = a
return ep_reward
def evaluate(actor_main, env, control_stepsize, state_dim,action_dim,actions_per_control ,video_info = None):
action_stepsize = int(control_stepsize / actions_per_control)
timestep = env.reset()
_, _, _, s = timestep
prev_action = np.zeros([actions_per_control,action_dim])
s = utils.state_1d_flat(s)
s_a = np.append(s, prev_action.reshape([-1]))
s_a = torch.FloatTensor(s_a).to(device)
step_i = 0
ep_reward = 0
if video_info is not None:
video_dir = video_info[0]
epi_i = video_info[1]
video_filepath = os.path.join(video_dir,"training_"+str(epi_i)+".avi")
video_saver = utils.VideoSaver(video_filepath, int(1.0/env.control_timestep()), 30, width=320, height=240)
frame = env.physics.render(camera_id=0, width=320,height=240)
video_saver.write(utils.RGB2BGR(frame))
while step_i < 1000:
with torch.no_grad():
a = actor_main.forward(s_a.view(-1,state_dim)).cpu().numpy()[0]
actions = a.reshape([actions_per_control,action_dim])
for action_iter in range(actions_per_control):
for _ in range(action_stepsize):
timestep = env.step(prev_action[action_iter])
step_i += 1
if video_info is not None:
frame = env.physics.render(camera_id=0, width=320, height=240)
video_saver.write(utils.RGB2BGR(frame))
if step_i > 1000:
break
if step_i > 1000:
break
if step_i > 1000:
break
t, r, _, s2 = timestep
s2 = utils.state_1d_flat(s2)
s2_a = np.append(s2, actions.reshape([-1]))
s2_a = torch.FloatTensor(s2_a).to(device)
s_a = s2_a
ep_reward += r
prev_action = actions
if video_info is not None:
video_saver.release()
return ep_reward
def evaluate(actor_main,
env,
control_stepsize,
state_dim,
action_dim,
video_info=None):
timestep = env.reset()
_, _, _, s = timestep
s = torch.FloatTensor(utils.state_1d_flat(s)).to(device)
step_i = 0
ep_reward = 0
if video_info is not None:
video_dir = video_info[0]
epi_i = video_info[1]
video_filepath = os.path.join(video_dir,
"training_" + str(epi_i) + ".avi")
video_saver = utils.VideoSaver(video_filepath,
int(1.0 / env.control_timestep()),
30,
width=320,
height=240)
frame = env.physics.render(camera_id=0, width=320, height=240)
video_saver.write(utils.RGB2BGR(frame))
while step_i < 1000:
with torch.no_grad():
a = actor_main.forward(s.view(-1, state_dim)).cpu().numpy()[0]
for _ in range(control_stepsize):
timestep = env.step(np.reshape(a, (action_dim, )))
step_i += 1
if video_info is not None:
frame = env.physics.render(camera_id=0, width=320, height=240)
video_saver.write(utils.RGB2BGR(frame))
if step_i > 1000:
break
if step_i > 1000:
break
t, r, _, s2 = timestep
s2 = torch.FloatTensor(utils.state_1d_flat(s2)).to(device)
s = s2
ep_reward += r
if video_info is not None:
video_saver.release()
return ep_reward
assert noise_type in ["ou", "gaussian"]
if noise_type == "ou":
noise = Noise.OrnsteinUhlenbeckActionNoise(
mu=np.zeros([action_dim]), sigma=sigma * np.ones([action_dim]))
else:
noise = Noise.GaussianNoise(action_dim=action_dim, sigma=sigma)
noise.reset()
timestep = env.reset()
ep_reward = 0.0
prev_action = np.zeros([action_dim])
# timestep, reward, discount, observation
_, _, _, s = timestep
s = utils.state_1d_flat(s)
s_a = np.append(s, prev_action)
s_a = torch.FloatTensor(s_a).to(device)
# for recording
if epi_i % video_save_period == 1:
video_filepath = os.path.join(
record_dir, "training_noise_" + str(epi_i) + ".avi")
video_saver = utils.VideoSaver(video_filepath,
int(1.0 / env.control_timestep()),
30,
width=320,
height=240)
frame = env.physics.render(camera_id=0, width=320, height=240)
assert noise_type in ["ou", "gaussian"]
if noise_type == "ou":
noise = Noise.OrnsteinUhlenbeckActionNoise(
mu=np.zeros([action_dim]), sigma=sigma * np.ones([action_dim]))
else:
noise = Noise.GaussianNoise(action_dim=action_dim, sigma=sigma)
noise.reset()
timestep = env.reset()
ep_reward = 0.0
prev_action = np.zeros([action_dim])
# timestep, reward, discount, observation
_, _, _, s = timestep
s = torch.FloatTensor(utils.state_1d_flat(s)).to(device)
if epi_i % video_save_period == 1:
video_filepath = os.path.join(
record_dir, "training_noise_" + str(epi_i) + ".avi")
video_saver = utils.VideoSaver(video_filepath,
int(1.0 / env.control_timestep()),
30,
width=320,
height=240)
frame = env.physics.render(camera_id=0, width=320, height=240)
video_saver.write(utils.RGB2BGR(frame))
step_i = 0
while step_i < 1000:
