def run(config):
model_path = (Path('./models') / config.env_id / config.model_name /
('run%i' % config.run_num))
if config.incremental is not None:
model_path = model_path / 'incremental' / ('model_ep%i.pt' %
config.incremental)
else:
model_path = model_path / 'model.pt'
if config.save_gifs:
gif_path = model_path.parent / 'gifs' if not config.mixed_policies else model_path.parent / 'gifs_mixed'
gif_path.mkdir(exist_ok=True)
torch.manual_seed(config.seed)
np.random.seed(config.seed)
if config.mixed_policies:
maddpg = MADDPG.init_from_directory(
Path('./models') / config.env_id / config.model_name)
else:
maddpg = MADDPG.init_from_save(model_path)
env = make_env(config.env_id,
benchmark=True,
discrete_action=maddpg.discrete_action)
env.world.seed(config.seed)
maddpg.prep_rollouts(device='cpu')
ifi = 1 / config.fps # inter-frame interval
all_infos = np.empty(
(config.n_episodes, config.episode_length, maddpg.nagents, 10))
all_positions = np.zeros(
(config.n_episodes, config.episode_length, maddpg.nagents, 2))
for ep_i in range(config.n_episodes):
print("Episode %i of %i" % (ep_i + 1, config.n_episodes))
obs = env.reset()
if config.save_gifs:
frames = []
frames.append(env.render('rgb_array')[0])
env.render('human')
for t_i in range(config.episode_length):
calc_start = time.time()
# rearrange observations to be per agent, and convert to torch Variable
torch_obs = [
Variable(torch.Tensor(obs[i]).view(1, -1), requires_grad=False)
if not obs[i].ndim == 4 else Variable(torch.Tensor(obs[i]),
requires_grad=False)
for i in range(maddpg.nagents)
]
all_positions[ep_i, t_i] = env.get_positions()
# get actions as torch Variables
torch_actions = maddpg.step(torch_obs, explore=False)
# convert actions to numpy arrays
actions = [ac.data.numpy().flatten() for ac in torch_actions]
obs, rewards, dones, infos = env.step(actions)
if config.save_gifs:
frames.append(env.render('rgb_array')[0])
# frames.append(env.world.viewers[0].render(return_rgb_array = True)) uncomment if local views visible
calc_end = time.time()
elapsed = calc_end - calc_start
if elapsed < ifi:
time.sleep(ifi - elapsed)
env.render('human')
if len(np.array(infos['n']).shape) < 4:
all_infos[ep_i,
t_i, :, :len(infos['n'][-1])] = np.array(infos['n'])
if config.save_gifs:
gif_num = 0
while (gif_path / ('%i_%i.gif' % (gif_num, ep_i))).exists():
gif_num += 1
imageio.mimsave(str(gif_path / ('%i_%i.gif' % (gif_num, ep_i))),
frames,
duration=ifi)
env.close()
if config.save_stats:
stats_path = model_path.parent / 'stats' if not config.mixed_policies else model_path.parent / 'stats_mixed'
stats_path.mkdir(exist_ok=True)
save(f'{stats_path}/all_infos.npy', all_infos)
save(f'{stats_path}/all_positions.npy', all_positions)
def run(config):
model_path = (Path('../models') / config.env_id / config.model_name /
('run%i' % config.run_num))
if config.incremental is not None:
model_path = model_path / 'incremental' / ('model_ep%i.pt' %
config.incremental)
else:
model_path = model_path / 'model.pt'
gif_path = model_path.parent / 'stats' if not config.mixed_policies else model_path.parent / 'stats_mixed'
gif_path.mkdir(exist_ok=True)
torch.manual_seed(config.seed)
np.random.seed(config.seed)
if config.mixed_policies:
maddpg = MADDPG.init_from_directory(
Path('../models') / config.env_id / config.model_name)
else:
maddpg = MADDPG.init_from_save(model_path)
env = make_env(config.env_id,
benchmark=True,
discrete_action=maddpg.discrete_action)
env.seed(config.seed)
maddpg.prep_rollouts(device='cpu')
ifi = 1 / config.fps # inter-frame interval
all_infos = np.empty(
(config.n_episodes, config.episode_length, maddpg.nagents, 10))
n_movable_agents = sum([1 if a.movable else 0 for a in env.agents])
n_speaking_agents = sum([0 if a.silent else 1 for a in env.agents])
all_positions = np.zeros((config.n_episodes, config.episode_length,
n_movable_agents, env.world.dim_p))
all_communications = np.zeros((config.n_episodes, config.episode_length,
n_speaking_agents, env.world.dim_c))
all_actions = np.zeros((config.n_episodes, config.episode_length,
len(env.agents), env.world.dim_c))
obs_space = sum([obsp.shape[0] for obsp in env.observation_space])
all_obs = np.zeros((config.n_episodes, config.episode_length, obs_space))
for ep_i in range(config.n_episodes):
print("Episode %i of %i" % (ep_i + 1, config.n_episodes))
obs = env.reset()
# env.agents[1].state.p_pos = np.array([0., 0.])
for t_i in range(config.episode_length):
calc_start = time.time()
# rearrange observations to be per agent, and convert to torch Variable
torch_obs = [
Variable(torch.Tensor(obs[i]).view(1, -1), requires_grad=False)
if not obs[i].ndim == 4 else Variable(torch.Tensor(obs[i]),
requires_grad=False)
for i in range(maddpg.nagents)
]
all_positions[ep_i, t_i] = env.get_positions()
all_communications[ep_i, t_i] = env.get_communications()
# get actions as torch Variables
torch_actions = maddpg.step(torch_obs, explore=False)
# convert actions to numpy arrays
actions = [ac.data.numpy().flatten() for ac in torch_actions]
# actions[0] = np.array([0., 0., 0., 0., 0.], dtype=np.float32)
# actions[0][ep_i] = 1.
obs, rewards, dones, infos = env.step(actions)
all_actions[ep_i, t_i, :, :] = actions
all_obs[ep_i, t_i, :] = np.concatenate(np.asarray(obs))
calc_end = time.time()
elapsed = calc_end - calc_start
if elapsed < ifi:
time.sleep(ifi - elapsed)
if len(np.array(infos['n']).shape) < 4:
all_infos[ep_i,
t_i, :, :len(infos['n'][-1])] = np.array(infos['n'])
env.close()
if config.save_stats:
stats_path = model_path.parent / 'stats' if not config.mixed_policies else model_path.parent / 'stats_mixed'
stats_path.mkdir(exist_ok=True)
save(f'{stats_path}/all_infos.npy', all_infos)
save(f'{stats_path}/all_positions.npy', all_positions)
save(f'{stats_path}/all_communications.npy', all_communications)
save(f'{stats_path}/all_actions.npy', all_actions)
save(f'{stats_path}/all_observations.npy', all_obs)
