'obs gain forward', 'obs gain angular', 'obs noise std forward',
'obs noise std angular', 'goal radius', 'box_size', 'discount_factor'
]
history = pd.DataFrame(columns=COLUMNS)
while episode <= MAX_EPISODE:
episode += 1 # every episode starts a new firefly
t = torch.zeros(1) # to track the amount of time steps to catch a firefly
theta = (pro_gains, pro_noise_stds, obs_gains, obs_noise_stds, goal_radius)
env.Brender(b, x, arg.WORLD_SIZE, goal_radius
) # display pop-up window (for every new action in each step)
while t < arg.EPISODE_LEN: # for a single FF
action = agent.select_action(state, action_noise=noise,
param=None) # with action noise
next_x, reached_target, next_b, reward, info, next_state = env.step(
episode, x, b, action, t, theta, arg.REWARD)
env.Brender(
next_b, next_x, arg.WORLD_SIZE, goal_radius
) # display pop-up window (for every new action in each step)
#time.sleep(0.1) # delay for 0.005 sec
if info['stop']:
time.sleep(1)
# check time limit
TimeEnd = (
t + 1 == arg.EPISODE_LEN
) # if the monkey can't catch the firefly in EPISODE_LEN, reset the game.
mask = torch.tensor([1 - float(TimeEnd)]) # mask = 0: episode is over
rew_std,
state_dim,
action_dim,
hidden_dim=128,
tau=0.001)
agent.load('pretrained/ddpg_minhae/ddpg_model_EE.pth.tar')
tot_t = 0.
episode = 0.
while tot_t <= TOT_T:
episode += 1 # every episode starts a new firefly
t, x, P, ox, b, state = env.reset()
episode_reward = 0.
while t < EPISODE_LEN:
action = agent.select_action(state, noise)
next_x, reached_target, next_b, reward, info, next_state = env.step(
episode, x, b, action, t)
env.Brender(
next_b, next_x
) # display pop-up window (for every new action in each step)
#rec.capture_frame() # for video
time.sleep(0.1) # delay for 0.005 sec
if info['stop']:
time.sleep(2)
# check time limit
TimeEnd = (
t + 1 == EPISODE_LEN
) # if the monkey can't catch the firefly in EPISODE_LEN, reset the game.