Created
July 11, 2023 08:05
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class DiffusionPolicy(tf.keras.Model): | |
def __init__(self, action_space: int): | |
super(DiffusionPolicy, self).__init__() | |
self.n_timesteps = 5 | |
self.action_space = action_space | |
self.time_embedding = SinusoidalPositionalEmbedding(L=self.n_timesteps, D=12) | |
self.dense1 = kl.Dense(256, activation=mish) | |
self.dense2 = kl.Dense(256, activation=mish) | |
self.dense3 = kl.Dense(256, activation=mish) | |
self.out = kl.Dense(self.action_space, activation=None) | |
self.alphas, self.betas = get_noise_schedule(T=self.n_timesteps) | |
self.alphas_cumprod = tf.math.cumprod(self.alphas) | |
self.alphas_cumprod_prev = tf.concat([[1.], self.alphas_cumprod[:-1]], axis=0) | |
self.variance = self.betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) | |
def call(self, x, timesteps, states): | |
t = self.time_embedding(timesteps) | |
x = tf.concat([x, t, states], axis=1) | |
x = self.dense1(x) | |
x = self.dense2(x) | |
x = self.dense3(x) | |
eps = self.out(x) | |
return eps | |
@tf.function | |
def compute_bc_loss(self, actions, states): | |
x_0 = actions | |
batch_size = x_0.shape[0] | |
timesteps = tf.random.uniform(shape=(batch_size, 1), minval=0, maxval=self.n_timesteps, dtype=tf.int32), | |
alphas_cumprod_t = tf.reshape(tf.gather(self.alphas_cumprod, indices=timesteps), (-1, 1)) # (1, B, 1) -> (B, 1) | |
eps = tf.random.normal(shape=x_0.shape, mean=0., stddev=1.) | |
x_t = tf.sqrt(alphas_cumprod_t) * x_0 + tf.sqrt(1. - alphas_cumprod_t) * eps | |
eps_pred = self(x_t, timesteps, states) | |
bc_loss = tf.reduce_mean(tf.square(eps - eps_pred)) | |
return bc_loss | |
@tf.function | |
def sample_actions(self, states): | |
batch_size = states.shape[0] | |
x_t = tf.random.normal(shape=(batch_size, self.action_space), mean=0., stddev=1.) | |
for t in reversed(range(0, self.n_timesteps)): | |
t = t * tf.ones(shape=(batch_size, 1), dtype=tf.int32) # (B, 1) | |
x_t = self.inv_diffusion(x_t, t, states) | |
x_0 = tf.clip_by_value(x_t, -1.0, 1.0) | |
return x_0 | |
def inv_diffusion(self, x_t, t, states): | |
beta_t = tf.reshape(tf.gather(self.betas, indices=t), (-1, 1)) # (1, B, 1) -> (B, 1) | |
alphas_cumprod_t = tf.reshape(tf.gather(self.alphas_cumprod, indices=t), (-1, 1)) # (1, B, 1) -> (B, 1) | |
eps_t = self(x_t, t, states) | |
mu = (1.0 / tf.sqrt(1.0 - beta_t)) * (x_t - (beta_t / tf.sqrt(1.0 - alphas_cumprod_t)) * eps_t) | |
sigma = tf.sqrt(tf.reshape(tf.gather(self.variance, indices=t), (-1, 1))) | |
noise = tf.random.normal(shape=x_t.shape, mean=0., stddev=1.) | |
x_t_minus_1 = mu + sigma * noise | |
return x_t_minus_1 |
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