def __init__(self, env, feature_str, network_path, device, **kwargs):
self.env = env
self.feature = feature_factory(feature_str)
self.device = device
self.network = None
if network_path:
self.load_network(network_path)
def main(load_memory, generate_envs, feature_str, gamma, ftq_params, ftq_net_params, device, normalize_reward,
workspace, seed,
lambda_=0., **args):
envs, params = envs_factory.generate_envs(**generate_envs)
e = envs[0]
set_seed(seed, e)
feature = feature_factory(feature_str)
ftq = PytorchFittedQ(
device=device,
policy_network=NetFTQ(n_in=len(feature(e.reset(), e)), n_out=e.action_space.n, **ftq_net_params),
action_str=None if not hasattr(e, "action_str") else e.action_str,
test_policy=None,
gamma=gamma,
**ftq_params
)
rm = Memory()
rm.load_memory(**load_memory)
transitions_ftq, _ = urpy.datas_to_transitions(rm.memory, e, feature, lambda_, normalize_reward)
logger.info("[learning ftq with full batch] #samples={} ".format(len(transitions_ftq)))
ftq.reset(True)
ftq.workspace = workspace
makedirs(ftq.workspace)
ftq.fit(transitions_ftq)
ftq.save_policy()
def __init__(self,
env,
feature_str,
network_path,
betas_for_discretisation,
device,
hull_options,
clamp_Qc=False,
**kwargs):
self.env = env
self.feature = feature_factory(feature_str)
self.betas_for_discretisation = betas_for_discretisation
self.device = device
self.network = None
self.hull_options = hull_options
self.clamp_Qc = clamp_Qc
if network_path:
self.load_network(network_path)
def main(load_memory, generate_envs, feature_str, gamma, gamma_c, bftq_params,
bftq_net_params, workspace, seed, device, normalize_reward, general,
**args):
logger = logging.getLogger(__name__)
envs, params = envs_factory.generate_envs(**generate_envs)
e = envs[0]
e.reset()
set_seed(seed, e)
feature = feature_factory(feature_str)
bftq = PytorchBudgetedFittedQ(
device=device,
workspace=workspace,
actions_str=get_actions_str(e),
policy_network=NetBFTQ(size_state=len(feature(e.reset(), e)),
n_actions=e.action_space.n,
**bftq_net_params),
gamma=gamma,
gamma_c=gamma_c,
split_batches=general["gpu"]["split_batches"],
cpu_processes=general["cpu"]["processes"],
env=e,
**bftq_params)
makedirs(workspace)
rm = Memory()
rm.load_memory(**load_memory)
_, transitions_bftq = urpy.datas_to_transitions(rm.memory, e, feature, 0,
normalize_reward)
logger.info("[learning bftq with full batch] #samples={} ".format(
len(transitions_bftq)))
bftq.reset(True)
_ = bftq.fit(transitions_bftq)
bftq.save_policy()
def main(generate_envs, feature_str, betas_for_exploration, gamma, gamma_c,
bftq_params, bftq_net_params, N_trajs, workspace, seed, device,
normalize_reward, trajs_by_ftq_batch, epsilon_decay, general, **args):
# Prepare BFTQ
envs, params = envs_factory.generate_envs(**generate_envs)
e = envs[0]
set_seed(seed, e)
rm = Memory()
feature = feature_factory(feature_str)
def build_fresh_bftq():
bftq = PytorchBudgetedFittedQ(
device=device,
workspace=workspace / "batch=0",
actions_str=get_actions_str(e),
policy_network=NetBFTQ(size_state=len(feature(e.reset(), e)),
n_actions=e.action_space.n,
**bftq_net_params),
gamma=gamma,
gamma_c=gamma_c,
cpu_processes=general["cpu"]["processes"],
env=e,
split_batches=general["gpu"]["split_batches"],
hull_options=general["hull_options"],
**bftq_params)
return bftq
# Prepare learning
i_traj = 0
decays = math_utils.epsilon_decay(**epsilon_decay,
N=N_trajs,
savepath=workspace)
betas_for_exploration = np.array(eval(betas_for_exploration))
memory_by_batch = [get_current_memory()]
batch_sizes = near_split(N_trajs, size_bins=trajs_by_ftq_batch)
pi_epsilon_greedy_config = {
"__class__": repr(EpsilonGreedyPolicy),
"pi_greedy": {
"__class__": repr(RandomBudgetedPolicy)
},
"pi_random": {
"__class__": repr(RandomBudgetedPolicy)
},
"epsilon": decays[0],
"hull_options": general["hull_options"],
"clamp_Qc": bftq_params["clamp_Qc"]
}
# Main loop
trajs = []
for batch, batch_size in enumerate(batch_sizes):
# Prepare workers
cpu_processes = min(
general["cpu"]["processes_when_linked_with_gpu"] or os.cpu_count(),
batch_size)
workers_n_trajectories = near_split(batch_size, cpu_processes)
workers_start = np.cumsum(workers_n_trajectories)
workers_traj_indexes = [
np.arange(*times) for times in zip(
np.insert(workers_start[:-1], 0, 0), workers_start)
]
if betas_for_exploration.size:
workers_betas = [
betas_for_exploration.take(indexes, mode='wrap')
for indexes in workers_traj_indexes
]
else:
workers_betas = [
np.random.random(indexes.size)
for indexes in workers_traj_indexes
]
workers_seeds = np.random.randint(0, 10000, cpu_processes).tolist()
workers_epsilons = [
decays[i_traj + indexes] for indexes in workers_traj_indexes
]
workers_params = list(
zip_with_singletons(generate_envs, pi_epsilon_greedy_config,
workers_seeds, gamma, gamma_c,
workers_n_trajectories, workers_betas,
workers_epsilons, None, general["dictConfig"]))
# Collect trajectories
logger.info(
"Collecting trajectories with {} workers...".format(cpu_processes))
if cpu_processes == 1:
results = []
for params in workers_params:
results.append(execute_policy_from_config(*params))
else:
with Pool(processes=cpu_processes) as pool:
results = pool.starmap(execute_policy_from_config,
workers_params)
i_traj += sum([len(trajectories) for trajectories, _ in results])
# Fill memory
[
rm.push(*sample) for trajectories, _ in results
for trajectory in trajectories for sample in trajectory
]
transitions_ftq, transition_bftq = datas_to_transitions(
rm.memory, e, feature, 0, normalize_reward)
# Fit model
logger.info(
"[BATCH={}]---------------------------------------".format(batch))
logger.info(
"[BATCH={}][learning bftq pi greedy] #samples={} #traj={}".format(
batch, len(transition_bftq), i_traj))
logger.info(
"[BATCH={}]---------------------------------------".format(batch))
bftq = build_fresh_bftq()
bftq.reset(True)
bftq.workspace = workspace / "batch={}".format(batch)
makedirs(bftq.workspace)
if isinstance(e, EnvGridWorld):
for trajectories, _ in results:
for traj in trajectories:
trajs.append(traj)
w = World(e)
w.draw_frame()
w.draw_lattice()
w.draw_cases()
w.draw_source_trajectories(trajs)
w.save((bftq.workspace / "bftq_on_2dworld_sources").as_posix())
q = bftq.fit(transition_bftq)
# Save policy
network_path = bftq.save_policy()
os.system("cp {}/policy.pt {}/policy.pt".format(
bftq.workspace, workspace))
# Save memory
save_memory(bftq, memory_by_batch, by_batch=False)
# Update greedy policy
pi_epsilon_greedy_config["pi_greedy"] = {
"__class__": repr(PytorchBudgetedFittedPolicy),
"feature_str": feature_str,
"network_path": network_path,
"betas_for_discretisation": bftq.betas_for_discretisation,
"device": bftq.device,
"hull_options": general["hull_options"],
"clamp_Qc": bftq_params["clamp_Qc"]
}
if isinstance(e, EnvGridWorld):
def pi(state, beta):
import torch
from ncarrara.budgeted_rl.bftq.pytorch_budgeted_fittedq import convex_hull, \
optimal_pia_pib
with torch.no_grad():
hull = convex_hull(s=torch.tensor([state],
device=device,
dtype=torch.float32),
Q=q,
action_mask=np.zeros(e.action_space.n),
id="run_" + str(state),
disp=False,
betas=bftq.betas_for_discretisation,
device=device,
hull_options=general["hull_options"],
clamp_Qc=bftq_params["clamp_Qc"])
opt, _ = optimal_pia_pib(beta=beta,
hull=hull,
statistic={})
return opt
def qr(state, a, beta):
import torch
s = torch.tensor([[state]], device=device)
b = torch.tensor([[[beta]]], device=device)
sb = torch.cat((s, b), dim=2)
return q(sb).squeeze()[a]
def qc(state, a, beta):
import torch
s = torch.tensor([[state]], device=device)
b = torch.tensor([[[beta]]], device=device)
sb = torch.cat((s, b), dim=2)
return q(sb).squeeze()[e.action_space.n + a]
w = World(e, bftq.betas_for_discretisation)
w.draw_frame()
w.draw_lattice()
w.draw_cases()
w.draw_policy_bftq(pi, qr, qc, bftq.betas_for_discretisation)
w.save((bftq.workspace / "bftq_on_2dworld").as_posix())
save_memory(bftq, memory_by_batch, by_batch=True)
def main(generate_envs,
feature_str,
gamma,
gamma_c,
ftq_params,
ftq_net_params,
device,
epsilon_decay,
N_trajs,
trajs_by_ftq_batch,
normalize_reward,
workspace,
seed,
save_memory,
general,
lambda_=0,
**args):
envs, params = envs_factory.generate_envs(**generate_envs)
e = envs[0]
set_seed(seed, e)
rm = Memory()
feature = feature_factory(feature_str)
def build_fresh_ftq():
ftq = PytorchFittedQ(
device=device,
policy_network=NetFTQ(n_in=len(feature(e.reset(), e)),
n_out=e.action_space.n,
**ftq_net_params),
action_str=None if not hasattr(e, "action_str") else e.action_str,
test_policy=None,
gamma=gamma,
**ftq_params)
return ftq
# Prepare learning
i_traj = 0
decays = math_utils.epsilon_decay(**epsilon_decay,
N=N_trajs,
savepath=workspace)
batch_sizes = near_split(N_trajs, size_bins=trajs_by_ftq_batch)
pi_epsilon_greedy_config = {
"__class__": repr(EpsilonGreedyPolicy),
"pi_greedy": {
"__class__": repr(RandomPolicy)
},
"pi_random": {
"__class__": repr(RandomPolicy)
},
"epsilon": decays[0]
}
# Main loop
trajs = []
for batch, batch_size in enumerate(batch_sizes):
# Prepare workers
cpu_processes = min(
general["cpu"]["processes_when_linked_with_gpu"] or os.cpu_count(),
batch_size)
workers_n_trajectories = near_split(batch_size, cpu_processes)
workers_start = np.cumsum(workers_n_trajectories)
workers_traj_indexes = [
np.arange(*times) for times in zip(
np.insert(workers_start[:-1], 0, 0), workers_start)
]
workers_seeds = np.random.randint(0, 10000, cpu_processes).tolist()
workers_epsilons = [
decays[i_traj + indexes] for indexes in workers_traj_indexes
]
workers_params = list(
zip_with_singletons(generate_envs, pi_epsilon_greedy_config,
workers_seeds, gamma, gamma_c,
workers_n_trajectories, None, workers_epsilons,
None, general["dictConfig"]))
# Collect trajectories
logger.info(
"Collecting trajectories with {} workers...".format(cpu_processes))
if cpu_processes == 1:
results = [execute_policy_from_config(*workers_params[0])]
else:
with Pool(processes=cpu_processes) as pool:
results = pool.starmap(execute_policy_from_config,
workers_params)
i_traj += sum([len(trajectories) for trajectories, _ in results])
# Fill memory
[
rm.push(*sample) for trajectories, _ in results
for trajectory in trajectories for sample in trajectory
]
transitions_ftq, _ = datas_to_transitions(rm.memory, e, feature,
lambda_, normalize_reward)
# Fit model
logger.info(
"[BATCH={}]---------------------------------------".format(batch))
logger.info(
"[BATCH={}][learning ftq pi greedy] #samples={} #traj={}".format(
batch, len(transitions_ftq), i_traj))
logger.info(
"[BATCH={}]---------------------------------------".format(batch))
ftq = build_fresh_ftq()
ftq.reset(True)
ftq.workspace = workspace / "batch={}".format(batch)
makedirs(ftq.workspace)
if isinstance(e, EnvGridWorld):
for trajectories, _ in results:
for traj in trajectories:
trajs.append(traj)
w = World(e)
w.draw_frame()
w.draw_lattice()
w.draw_cases()
w.draw_source_trajectories(trajs)
w.save((ftq.workspace / "bftq_on_2dworld_sources").as_posix())
ftq.fit(transitions_ftq)
# Save policy
network_path = ftq.save_policy()
os.system("cp {}/policy.pt {}/final_policy.pt".format(
ftq.workspace, workspace))
# Update greedy policy
pi_epsilon_greedy_config["pi_greedy"] = {
"__class__": repr(PytorchFittedPolicy),
"feature_str": feature_str,
"network_path": network_path,
"device": ftq.device
}
if save_memory is not None:
rm.save(workspace / save_memory["path"], save_memory["as_json"])
def main(policy_path, generate_envs, feature_str, device, workspace, bftq_params, seed, general,
betas_test, N_trajs, gamma, gamma_c, bftq_net_params, **args):
if not os.path.isabs(policy_path):
policy_path = workspace / policy_path
env = envs_factory.generate_envs(**generate_envs)[0][0]
feature = feature_factory(feature_str)
bftq = PytorchBudgetedFittedQ(
device=device,
workspace=workspace,
actions_str=get_actions_str(env),
policy_network=NetBFTQ(size_state=len(feature(env.reset(), env)), n_actions=env.action_space.n,
**bftq_net_params),
gamma=gamma,
gamma_c=gamma_c,
cpu_processes=general["cpu"]["processes"],
env=env,
hull_options=general["hull_options"],
**bftq_params)
bftq.reset(True)
pi_config = {
"__class__": repr(PytorchBudgetedFittedPolicy),
"feature_str": feature_str,
"network_path": policy_path,
"betas_for_discretisation": eval(bftq_params["betas_for_discretisation"]),
"device": device,
"hull_options": general["hull_options"],
"clamp_Qc": bftq_params["clamp_Qc"],
"env": env
}
pi = policy_factory(pi_config)
# Iterate over betas
for beta in eval(betas_test):
logger.info("Rendering with beta={}".format(beta))
set_seed(seed, env)
for traj in range(N_trajs):
done = False
pi.reset()
info_env = {}
info_pi = {"beta": beta}
t = 0
# Make a workspace for trajectories
traj_workspace = workspace / "trajs" / "beta={}".format(beta) / "traj={}".format(traj)
makedirs(traj_workspace)
bftq.workspace = traj_workspace
monitor = MonitorV2(env, traj_workspace, add_subdirectory=False)
obs = monitor.reset()
# Run trajectory
while not done:
action_mask = get_action_mask(env)
info_pi = merge_two_dicts(info_pi, info_env)
bftq.draw_Qr_and_Qc(obs, pi.network, "render_t={}".format(t), show=False)
a, _, info_pi = pi.execute(obs, action_mask, info_pi)
render(env, workspace, t, a)
obs, _, done, info_env = monitor.step(a)
t += 1
monitor.close()
def main(empty_previous_test=False):
# betas = np.linspace(0, 1, 5)
set_seed(C.seed)
if empty_previous_test:
empty_directory(C.path_ftq_results)
envs, params = generate_envs(**C["generate_envs"])
e = envs[0]
e.reset()
feature = feature_factory(C["feature_str"])
size_state = len(feature(e.reset(), e))
logger.info("neural net input size : {}".format(size_state))
N = C["create_data"]["N_trajs"]
traj_max_size = np.inf
decays = epsilon_decay(**C["create_data"]["epsilon_decay"], N=N)
net = BudgetedNetwork(size_state=size_state,
layers=C["bftq_net_params"]["intra_layers"] +
[2 * e.action_space.n],
device=C.device,
**C["bftq_net_params"])
betas_for_discretisation = eval(C["betas_for_discretisation"])
betas = np.linspace(0, 1, 31)
print(C["bdqn_params"])
dqn = PytorchBudgetedDQN(policy_net=net,
workspace=C.path_bdqn,
device=C.device,
gamma=C["gamma"],
gamma_c=C["gamma_c"],
beta_for_discretisation=betas_for_discretisation,
**C["bdqn_params"])
dqn.reset()
e.seed(C.seed)
rrr = []
rrr_greedy = []
nb_samples = 0
rm = Memory()
result = np.zeros((N, 4))
for n in range(N):
if len(betas) > 0:
betas_to_explore = betas
else:
betas_to_explore = [np.random.random()]
for beta in betas_to_explore:
logger.info("beta {}".format(beta))
if N // 10 > 0 and n % (N // 10) == 0:
logger.debug("DQN step {}/{}".format(n, N))
s = e.reset()
done = False
result_traj = np.zeros(4)
it = 0
trajectory = []
while (not done):
if np.random.random() < decays[n]:
if hasattr(e, "action_space_executable"):
raise NotImplementedError("TODO")
else:
action_repartition = np.random.random(e.action_space.n)
action_repartition /= np.sum(action_repartition)
budget_repartion = generate_random_point_on_simplex_not_uniform(
coeff=action_repartition,
bias=beta,
min_x=0,
max_x=1)
a = np.random.choice(a=range(e.action_space.n),
p=action_repartition)
beta_ = budget_repartion[a]
logger.info(
"Random action : {} with a random budget : {:.2f} (from {})"
.format(a, beta_, [
"{:.2f}".format(bud)
for bud in budget_repartion
]))
else:
if hasattr(e, "action_space_executable"):
raise NotImplementedError("TODO")
else:
a, beta_ = dqn.pi(feature(s, e), beta,
np.zeros(e.action_space.n))
logger.info(
"Greedy action : {} with corresponding budget {}".
format(a, beta))
s_, r_, done, info = e.step(a)
c_ = info["c_"]
sample = (s, a if type(a) is str else int(a), r_, s_, done,
info)
trajectory.append(sample)
result_traj += np.array(
[r_, c_, r_ * (C["gamma"]**it), c_ * (C["gamma_c"]**it)])
t_dqn = (feature(s,
e), a, r_, feature(s_,
e), c_, beta, done, info)
dqn.update(*t_dqn)
s = s_
beta = beta_
nb_samples += 1
it += 1
if it % 100 == 0:
if it > 500:
logger.warning(
"Number of trajectories overflowing : {}".format(
it))
if traj_max_size is not None and it >= traj_max_size:
logger.warning("Max size trajectory reached")
break
logger.info("result_traj : {}".format(result_traj))
for sample in trajectory:
rm.push(*sample)
logger.info("[execute_policy] saving results at : {}".format(
C.path_dqn_results))
np.savetxt(C.path_bdqn_results + "/greedy_lambda_=0.result", result)
if N > 100:
nb_traj_packet = 100
a = np.reshape(rrr, (int(N / nb_traj_packet), -1))
a = np.mean(a, 1)
x = np.asarray(range(len(a))) * nb_traj_packet
plt.plot(x, a)
a = np.reshape(rrr_greedy, (int(N / nb_traj_packet), -1))
a = np.mean(a, 1)
plt.plot(x, a)
plt.title("dqn results")
plt.show()
plt.savefig(C.workspace / "dqn_create_data")
plt.close()
rm.save(C.workspace, "/" + C["create_data"]["filename_data"],
C["create_data"]["as_json"])
