def run_policy(env,
get_action,
ckpt_num,
con,
max_ep_len=100,
num_episodes=100,
render=False,
record=True,
video_caption_off=False):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
visual_obs = []
while n < num_episodes:
vob = render_frame(env,
ep_len,
ep_ret,
'AC',
render,
record,
caption_off=video_caption_off)
visual_obs.append(vob)
a = get_action(torch.Tensor(o.reshape(1, -1)))[0]
o, r, d, _ = env.step(a.detach().numpy()[0])
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
vob = render_frame(env,
ep_len,
ep_ret,
'AC',
render,
record,
caption_off=video_caption_off)
visual_obs.append(vob) # add last frame
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet %d' % con, with_min_and_max=True)
logger.log_tabular('EpLen %d' % con, average_only=True)
logger.dump_tabular()
if record:
# temp_info: [video_prefix, ckpt_num, ep_ret, ep_len, con]
temp_info = ['', ckpt_num, ep_ret, ep_len, con]
logger.save_video(visual_obs, temp_info)
def run_policy(env,
get_action,
ckpt_num,
max_con,
con,
max_ep_len=100,
num_episodes=100,
fpath=None,
render=False,
record=True,
video_caption_off=False):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
output_dir = osp.join(osp.abspath(osp.dirname(osp.dirname(__file__))),
"log/tmp/experiments/%i" % int(time.time()))
logger = EpochLogger(output_dir=output_dir)
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
visual_obs = []
c_onehot = F.one_hot(torch.tensor(con), max_con).squeeze().float()
while n < num_episodes:
vob = render_frame(env,
ep_len,
ep_ret,
'AC',
render,
record,
caption_off=video_caption_off)
visual_obs.append(vob)
concat_obs = torch.cat(
[torch.Tensor(o.reshape(1, -1)),
c_onehot.reshape(1, -1)], 1)
a = get_action(concat_obs)
o, r, d, _ = env.step(a[0].detach().numpy()[0])
ep_ret += r
ep_len += 1
d = False
if d or (ep_len == max_ep_len):
vob = render_frame(env,
ep_len,
ep_ret,
'AC',
render,
record,
caption_off=video_caption_off)
visual_obs.append(vob) # add last frame
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
if record:
# temp_info: [video_prefix, ckpt_num, ep_ret, ep_len, con]
temp_info = ['', ckpt_num, ep_ret, ep_len, con]
logger.save_video(visual_obs, temp_info, fpath)