def fill_buffer_with_expert(replay_buffer, env_name, epsilon=0.01):
mbsize = ARGS.mbsize
envs = [AtariEnv(env_name) for i in range(mbsize)]
num_act = envs[0].num_actions
nhid = 32
_, theta_q, Qf, _ = nn.build(
nn.conv2d(4, nhid, 8, stride=4), # Input is 84x84
nn.conv2d(nhid, nhid * 2, 4, stride=2),
nn.conv2d(nhid * 2, nhid * 2, 3),
nn.flatten(),
nn.hidden(nhid * 2 * 12 * 12, nhid * 16),
nn.linear(nhid * 16, num_act),
)
theta_q_trained = load_parameters_from_checkpoint()
if ARGS.expert_is_self:
theta_expert = theta_q_trained
else:
expert_id = {
'ms_pacman':457, 'asterix':403, 'seaquest':428}[env_name]
with open(f'checkpoints/dqn_model_{expert_id}.pkl',
"rb") as f:
theta_expert = pickle.load(f)
theta_expert = [tf(i) for i in theta_expert]
obs = [i.reset() for i in envs]
trajs = [list() for i in range(mbsize)]
enumbers = list(range(mbsize))
replay_buffer.ram = torch.zeros([replay_buffer.size, 128],
dtype=torch.uint8,
device=replay_buffer.device)
while True:
mbobs = tf(obs) / 255
greedy_actions = Qf(mbobs, theta_expert).argmax(1)
random_actions = np.random.randint(0, num_act, mbsize)
actions = [
j if np.random.random() < epsilon else i
for i, j in zip(greedy_actions, random_actions)
]
for i, (e, a) in enumerate(zip(envs, actions)):
obsp, r, done, _ = e.step(a)
trajs[i].append([obs[i], int(a), float(r), int(done), e.getRAM() + 0])
obs[i] = obsp
if replay_buffer.idx + len(trajs[i]) + 4 >= replay_buffer.size:
# We're done!
return Qf, theta_q_trained
replay_buffer.new_episode(trajs[i][0][0], enumbers[i] % 2)
for s, a, r, d, ram in trajs[i]:
replay_buffer.ram[replay_buffer.idx] = tint(ram)
replay_buffer.add(s, a, r, d, enumbers[i] % 2)
trajs[i] = []
obs[i] = envs[i].reset()
enumbers[i] = max(enumbers) + 1
def main():
results = {
"episode": [],
"measure": [],
"parameters": [],
}
hps = {
"opt": ARGS.opt,
"env_name": ARGS.env_name,
"lr": ARGS.learning_rate,
"weight_decay": ARGS.weight_decay,
"run": ARGS.run,
}
start_step = ARGS.start_step
nhid = hps.get("nhid", 32)
gamma = hps.get("gamma", 0.99)
mbsize = hps.get("mbsize", 32)
weight_decay = hps.get("weight_decay", 0)
sample_near = hps.get("sample_near", "both")
slice_size = hps.get("slice_size", 0)
env_name = hps.get("env_name", "ms_pacman")
clone_interval = hps.get("clone_interval", 10_000)
reset_on_clone = hps.get("reset_on_clone", False)
reset_opt_on_clone = hps.get("reset_opt_on_clone", False)
max_clones = hps.get("max_clones", 2)
replay_type = hps.get("replay_type", "normal") # normal, prioritized
final_epsilon = hps.get("final_epsilon", 0.05)
num_exploration_steps = hps.get("num_exploration_steps", 500_000)
lr = hps.get("lr", 1e-4)
num_iterations = hps.get("num_iterations", 10_000_000)
buffer_size = hps.get("buffer_size", 250_000)
seed = hps.get("run", 0) + 1_642_559 # A large prime number
hps["_seed"] = seed
torch.manual_seed(seed)
np.random.seed(seed)
rng = np.random.RandomState(seed)
env = AtariEnv(env_name)
num_act = env.num_actions
# Define model
_Qarch, theta_q, Qf, _Qsemi = nn.build(
nn.conv2d(4, nhid, 8, stride=4), # Input is 84x84
nn.conv2d(nhid, nhid * 2, 4, stride=2),
nn.conv2d(nhid * 2, nhid * 2, 3),
nn.flatten(),
nn.hidden(nhid * 2 * 12 * 12, nhid * 16),
nn.linear(nhid * 16, num_act),
)
def make_opt():
if hps.get("opt", "sgd") == "sgd":
return torch.optim.SGD(theta_q, lr, weight_decay=weight_decay)
elif hps["opt"] == "msgd":
return torch.optim.SGD(theta_q,
lr,
momentum=hps.get("beta", 0.99),
weight_decay=weight_decay)
elif hps["opt"] == "rmsprop":
return torch.optim.RMSprop(theta_q, lr, weight_decay=weight_decay)
elif hps["opt"] == "adam":
return torch.optim.Adam(theta_q, lr, weight_decay=weight_decay)
else:
raise ValueError(hps["opt"])
opt = make_opt()
clone_theta_q = lambda: [i.detach().clone() for i in theta_q]
def copy_theta_q_to_target():
for i in range(len(theta_q)):
frozen_theta_q[i] = theta_q[i].detach().clone()
# Define loss
def sl1(a, b):
d = a - b
u = abs(d)
s = d**2
m = (u < s).float()
return u * m + s * (1 - m)
td = lambda s, a, r, sp, t, w, tw=theta_q: sl1(
r + (1 - t.float()) * gamma * Qf(sp, tw).max(1)[0].detach(),
Qf(s, w)[np.arange(len(a)), a.long()],
)
obs = env.reset()
if replay_type == "normal":
replay_buffer = ReplayBuffer(seed,
buffer_size,
near_strategy=sample_near)
elif replay_type == "prioritized":
replay_buffer = PrioritizedExperienceReplay(seed,
buffer_size,
near_strategy=sample_near)
total_reward = 0
last_end = 0
num_fill = 200000
num_measure = 500
_t0 = t0 = t1 = t2 = t3 = t4 = time.time()
tm0 = tm1 = tm2 = tm3 = time.time()
ema_loss = 0
last_rewards = [0]
measure = Measures()
print("Filling buffer")
if start_step < num_exploration_steps:
epsilon = 1 - (start_step / num_exploration_steps) * (1 -
final_epsilon)
else:
epsilon = final_epsilon
for it in range(num_fill):
if start_step == 0:
action = rng.randint(0, num_act)
else:
if rng.uniform(0, 1) < epsilon:
action = rng.randint(0, num_act)
else:
action = Qf(tf(obs / 255.0).unsqueeze(0)).argmax().item()
obsp, r, done, info = env.step(action)
replay_buffer.add(obs, action, r, done, env.enumber % 2)
if replay_type == "prioritized":
replay_buffer.set_last_priority(
td(
tf(obs / 255.0).unsqueeze(0),
tint([action]),
r,
tf(obsp / 255.0).unsqueeze(0),
tf([done]),
theta_q,
theta_q,
))
obs = obsp
if done:
obs = env.reset()
past_theta = [clone_theta_q()]
for it in range(start_step, num_iterations):
do_measure = not it % num_measure
eta = (time.time() - _t0) / (it + 1) * (num_iterations - it) / 60
if it and it % 100_000 == 0 or it == num_iterations - 1:
ps = {str(i): p.data.cpu().numpy() for i, p in enumerate(theta_q)}
ps.update({"step": it})
results["parameters"].append(ps)
if it % 10_000 == 0:
print(
it,
f"{(t1 - t0)*1000:.2f}, {(t2 - t1)*1000:.2f}, {(t3 - t2)*1000:.2f}, {(t4 - t3)*1000:.2f},",
f"{(tm1 - tm0)*1000:.2f}, {(tm3 - tm2)*1000:.2f},",
f"{int(eta//60):2d}h{int(eta%60):02d}m left",
f":: {ema_loss:.5f}, last 10 rewards: {np.mean(last_rewards):.2f}",
)
def main():
device = torch.device(ARGS.device)
nn.set_device(device)
results = {
"episode": [],
"measure": [],
"parameters": [],
}
hps = {
"opt": ARGS.opt,
"env_name": ARGS.env_name,
"lr": ARGS.learning_rate,
"weight_decay": ARGS.weight_decay,
"run": ARGS.run,
}
nhid = hps.get("nhid", 32)
gamma = hps.get("gamma", 0.99)
mbsize = ARGS.mbsize
weight_decay = hps.get("weight_decay", 0)
sample_near = hps.get("sample_near", "both")
slice_size = hps.get("slice_size", 0)
env_name = hps.get("env_name", "ms_pacman")
clone_interval = ARGS.clone_interval
reset_on_clone = hps.get("reset_on_clone", False)
reset_opt_on_clone = hps.get("reset_opt_on_clone", False)
max_clones = hps.get("max_clones", 2)
replay_type = hps.get("replay_type", "normal") # normal, prioritized
final_epsilon = hps.get("final_epsilon", 0.05)
num_exploration_steps = hps.get("num_exploration_steps", 500_000)
Lambda = ARGS.Lambda
lr = hps.get("lr", 1e-4)
num_iterations = hps.get("num_iterations", 10_000_000)
buffer_size = ARGS.buffer_size
seed = hps.get("run", 0) + 1_642_559 # A large prime number
hps["_seed"] = seed
torch.manual_seed(seed)
np.random.seed(seed)
rng = np.random.RandomState(seed)
env = AtariEnv(env_name)
num_act = env.num_actions
# Define model
_Qarch, theta_q, Qf, _Qsemi = nn.build(
nn.conv2d(4, nhid, 8, stride=4), # Input is 84x84
nn.conv2d(nhid, nhid * 2, 4, stride=2),
nn.conv2d(nhid * 2, nhid * 2, 3),
nn.flatten(),
nn.hidden(nhid * 2 * 12 * 12, nhid * 16),
nn.linear(nhid * 16, num_act),
)
def make_opt():
if hps.get("opt", "sgd") == "sgd":
return torch.optim.SGD(theta_q, lr, weight_decay=weight_decay)
elif hps["opt"] == "msgd":
return torch.optim.SGD(theta_q,
lr,
momentum=hps.get("beta", 0.99),
weight_decay=weight_decay)
elif hps["opt"] == "rmsprop":
return torch.optim.RMSprop(theta_q, lr, weight_decay=weight_decay)
elif hps["opt"] == "adam":
return torch.optim.Adam(theta_q, lr, weight_decay=weight_decay)
else:
raise ValueError(hps["opt"])
opt = make_opt()
clone_theta_q = lambda: [i.detach().clone() for i in theta_q]
def copy_theta_q_to_target():
for i in range(len(theta_q)):
frozen_theta_q[i] = theta_q[i].detach().clone()
# Define loss
def sl1(a, b):
d = a - b
u = abs(d)
s = d**2
m = (u < s).float()
return u * m + s * (1 - m)
td = lambda x: sl1(
x.r +
(1 - x.t.float()) * gamma * Qf(x.sp, past_theta[0]).max(1)[0].detach(),
Qf(x.s, theta_q)[np.arange(len(x.a)), x.a.long()],
)
tdQL = lambda x: sl1(
Qf(x.s, theta_q)[np.arange(len(x.a)), x.a.long()], x.lg)
mc = lambda x: sl1(Qf(x.s, theta_q).max(1)[0], x.g)
past_theta = [clone_theta_q()]
replay_buffer = ReplayBufferV2(seed, buffer_size, lambda s: Qf(s, theta_q),
lambda s: Qf(s, past_theta[0]).max(1)[0],
Lambda, gamma)
total_reward = 0
last_end = 0
num_fill = buffer_size // 2
num_measure = 500
_t0 = t0 = t1 = t2 = t3 = t4 = time.time()
tm0 = tm1 = tm2 = tm3 = time.time()
ema_loss = 0
last_rewards = [0]
measure = Measures(theta_q, {
"td": td,
"tdQL": tdQL,
"mc": mc,
}, replay_buffer, results["measure"], 32)
obs = env.reset()
for it in range(num_fill):
action = rng.randint(0, num_act)
obsp, r, done, info = env.step(action)
replay_buffer.add(obs, action, r, done)
obs = obsp
if done:
print(it)
obs = env.reset()
for it in range(num_iterations):
do_measure = not it % num_measure
eta = (time.time() - _t0) / (it + 1) * (num_iterations - it) / 60
if it and it % 100_000 == 0 or it == num_iterations - 1:
ps = {str(i): p.data.cpu().numpy() for i, p in enumerate(theta_q)}
ps.update({"step": it})
results["parameters"].append(ps)
if it < num_exploration_steps:
epsilon = 1 - (it / num_exploration_steps) * (1 - final_epsilon)
else:
epsilon = final_epsilon
if rng.uniform(0, 1) < epsilon:
action = rng.randint(0, num_act)
else:
action = Qf(tf(obs / 255.0).unsqueeze(0)).argmax().item()
obsp, r, done, info = env.step(action)
total_reward += r
replay_buffer.add(obs, action, r, done)
obs = obsp
if done:
obs = env.reset()
results["episode"].append({
"end": it,
"start": last_end,
"total_reward": total_reward
})
last_end = it
last_rewards = [total_reward] + last_rewards[:10]
total_reward = 0
sample = replay_buffer.sample(mbsize)
with torch.no_grad():
v_before = Qf(sample.s, theta_q).detach()
loss = tdQL(sample)
if do_measure:
tm0 = time.time()
measure.pre(sample)
tm1 = time.time()
loss = loss.mean()
loss.backward()
opt.step()
opt.zero_grad()
with torch.no_grad():
v_after = Qf(sample.s, theta_q).detach()
replay_buffer.compute_value_difference(sample, v_before, v_after)
if do_measure:
tm2 = time.time()
measure.post()
tm3 = time.time()
t4 = time.time()
if it and clone_interval and it % clone_interval == 0:
past_theta = [clone_theta_q()] #+ past_theta[:max_clones - 1]
replay_buffer.recompute_lambda_returns()
#exp_results["loss"].append(loss.item())
ema_loss = 0.999 * ema_loss + 0.001 * loss.item()
def main():
device = torch.device(ARGS.device)
mm.set_device(device)
results = {
"measure": [],
"parameters": [],
}
seed = ARGS.run + 1_642_559 # A large prime number
torch.manual_seed(seed)
np.random.seed(seed)
rng = np.random.RandomState(seed)
env = AtariEnv(ARGS.env_name)
mbsize = ARGS.mbsize
Lambda = ARGS.Lambda
nhid = 32
num_measure = 1000
gamma = 0.99
clone_interval = ARGS.clone_interval
num_iterations = ARGS.num_iterations
num_Q_outputs = 1
# Model
_Qarch, theta_q, Qf, _Qsemi = mm.build(
mm.conv2d(4, nhid, 8, stride=4), # Input is 84x84
mm.conv2d(nhid, nhid * 2, 4, stride=2),
mm.conv2d(nhid * 2, nhid * 2, 3),
mm.flatten(),
mm.hidden(nhid * 2 * 12 * 12, nhid * 16),
mm.linear(nhid * 16, num_Q_outputs),
)
clone_theta_q = lambda: [i.detach().clone() for i in theta_q]
theta_target = clone_theta_q()
opt = make_opt(ARGS.opt, theta_q, ARGS.learning_rate, ARGS.weight_decay)
# Replay Buffer
replay_buffer = ReplayBuffer(seed, ARGS.buffer_size)
# Losses
td = lambda s, a, r, sp, t, idx, w, tw: sl1(
r + (1 - t.float()) * gamma * Qf(sp, tw)[:, 0].detach(),
Qf(s, w)[:, 0],
)
tdL = lambda s, a, r, sp, t, idx, w, tw: sl1(
Qf(s, w)[:, 0], replay_buffer.LG[idx])
mc = lambda s, a, r, sp, t, idx, w, tw: sl1(
Qf(s, w)[:, 0], replay_buffer.g[idx])
# Define metrics
measure = Measures(
theta_q, {
"td": lambda x, w: td(*x, w, theta_target),
"tdL": lambda x, w: tdL(*x, w, theta_target),
"mc": lambda x, w: mc(*x, w, theta_target),
}, replay_buffer, results["measure"], 32)
# Get expert trajectories
rand_classes = fill_buffer_with_expert(env, replay_buffer)
# Compute initial values
replay_buffer.compute_values(lambda s: Qf(s, theta_q), num_Q_outputs)
replay_buffer.compute_returns(gamma)
replay_buffer.compute_reward_distances()
replay_buffer.compute_episode_boundaries()
replay_buffer.compute_lambda_returns(lambda s: Qf(s, theta_q), Lambda,
gamma)
# Run policy evaluation
for it in range(num_iterations):
do_measure = not it % num_measure
sample = replay_buffer.sample(mbsize)
if do_measure:
measure.pre(sample)
replay_buffer.compute_value_difference(sample, Qf(sample[0], theta_q))
loss = tdL(*sample, theta_q, theta_target)
loss = loss.mean()
loss.backward()
opt.step()
opt.zero_grad()
replay_buffer.update_values(sample, Qf(sample[0], theta_q))
if do_measure:
measure.post()
if it and clone_interval and it % clone_interval == 0:
theta_target = clone_theta_q()
replay_buffer.compute_lambda_returns(lambda s: Qf(s, theta_q),
Lambda, gamma)
if it and it % clone_interval == 0 or it == num_iterations - 1:
ps = {str(i): p.data.cpu().numpy() for i, p in enumerate(theta_q)}
ps.update({"step": it})
results["parameters"].append(ps)
with open(f'results/td_lambda_{run}.pkl', 'wb') as f:
pickle.dump(results, f)