def main():
gamma = 0.99
hps = pickle.load(open(ARGS.checkpoint, 'rb'))['hps']
env_name = hps["env_name"]
if 'Lambda' in hps:
Lambda = hps['Lambda']
else:
Lambda = 0
device = torch.device(ARGS.device)
nn.set_device(device)
replay_buffer = ReplayBuffer(ARGS.run, ARGS.buffer_size)
Qf, theta_q = fill_buffer_with_expert(replay_buffer, env_name)
for p in theta_q:
p.requires_grad = True
if Lambda > 0:
replay_buffer.compute_episode_boundaries()
replay_buffer.compute_lambda_returns(lambda s: Qf(s, theta_q), Lambda, gamma)
td__ = lambda s, a, r, sp, t, idx, w, tw: sl1(
r + (1 - t.float()) * gamma * Qf(sp, tw).max(1)[0].detach(),
Qf(s, w)[np.arange(len(a)), a.long()],
)
td = lambda s, a, r, sp, t, idx, w, tw: Qf(s, w).max(1)[0]
tdL = lambda s, a, r, sp, t, idx, w, tw: sl1(
Qf(s, w)[:, 0], replay_buffer.LG[idx])
loss_func = {
'td': td, 'tdL': tdL}[ARGS.loss_func]
opt = torch.optim.SGD(theta_q, 1)
def grad_sim(inp, grad):
dot = sum([(p.grad * gp).sum() for p, gp in zip(inp, grad)])
nA = torch.sqrt(sum([(p.grad**2).sum() for p, gp in zip(inp, grad)]))
nB = torch.sqrt(sum([(gp**2).sum() for p, gp in zip(inp, grad)]))
return (dot / (nA * nB)).item()
relevant_features = np.int32(
sorted(list(atari_dict[env_name.replace("_", "")].values())))
sims = []
ram_sims = []
for i in range(2000):
sim = []
*sample, idx = replay_buffer.sample(1)
loss = loss_func(*sample, idx, theta_q, theta_q).mean()
loss.backward()
g0 = [p.grad + 0 for p in theta_q]
for j in range(-30, 31):
opt.zero_grad()
loss = loss_func(*replay_buffer.get(idx + j), theta_q, theta_q).mean()
loss.backward()
sim.append(grad_sim(theta_q, g0))
sims.append(np.float32(sim))
for j in range(200):
opt.zero_grad()
*sample_j, idx_j = replay_buffer.sample(1)
loss = loss_func(*sample_j, idx_j, theta_q, theta_q).mean()
loss.backward()
ram_sims.append(
(grad_sim(theta_q, g0),
abs(replay_buffer.ram[idx[0]][relevant_features].float() -
replay_buffer.ram[idx_j[0]][relevant_features].float()).mean()))
opt.zero_grad()
ram_sims = np.float32(
ram_sims) #np.histogram(np.float32(ram_sim), 100, (-1, 1))
# Compute "True" gradient
grads = [i.detach() * 0 for i in theta_q]
N = 0
for samples in replay_buffer.in_order_iterate(ARGS.mbsize * 8):
loss = loss_func(*samples, theta_q, theta_q).mean()
loss.backward()
N += samples[0].shape[0]
for p, gp in zip(theta_q, grads):
gp.data.add_(p.grad)
opt.zero_grad()
dots = []
i = 0
for sample in replay_buffer.in_order_iterate(1):
loss = loss_func(*sample, theta_q, theta_q).mean()
loss.backward()
dots.append(grad_sim(theta_q, grads))
opt.zero_grad()
i += 1
histo = np.histogram(dots, 100, (-1, 1))
results = {
"grads": [i.cpu().data.numpy() for i in grads],
"sims": np.float32(sims),
"histo": histo,
"ram_sims": ram_sims,
}
path = f'results/grads_{ARGS.checkpoint}.pkl'
with open(path, "wb") as f:
pickle.dump(results, f)
