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Replace Tanh with ReLU of scores and simplify matrix operations

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This commit is contained in:
TJU_Lu
2024-12-22 15:43:04 +08:00
parent d21a2f818b
commit 43cf7bea6d
3 changed files with 45 additions and 90 deletions
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28
flightpolicy/yopo/yopo_algorithm.py
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@@ -157,8 +157,7 @@ class YopoAlgorithm:
if log_interval is not None and n_updates % log_interval[0] == 0:
self.logger.record("time/epoch", epoch_, exclude="tensorboard")
self.logger.record("time/steps", n_updates, exclude="tensorboard")
self.logger.record("time/batch_fps", log_interval[0] / (time.time() - start_time),
exclude="tensorboard")
self.logger.record("time/batch_fps", log_interval[0] / (time.time() - start_time), exclude="tensorboard")
self.logger.record("train/trajectory_cost", np.mean(cost_losses))
self.logger.record("train/score_loss", np.mean(score_losses))
self.logger.dump(step=n_updates)
@@ -260,6 +259,7 @@ class YopoAlgorithm:
costs.append(rew)
ep_len += 1
print("round ", n_roll, ", total steps:", len(costs), ", avg cost:", sum(costs) / len(costs))
self.env.disconnectUnity()
def train(self, gradient_steps: int, batch_size: int) -> None:
"""
@@ -364,25 +364,17 @@ class YopoAlgorithm:
convert the observation from body frame to primitive frame,
and then concatenate it with the depth features (to ensure the translational invariance)
"""
obs_return = np.ones(
(obs.shape[0], self.lattice_space.vertical_num, self.lattice_space.horizon_num, obs.shape[1]),
dtype=np.float32)
obs_return = np.ones((obs.shape[0], obs.shape[1], self.lattice_space.vertical_num, self.lattice_space.horizon_num), dtype=np.float32)
id = 0
v_b = obs[:, 0:3]
a_b = obs[:, 3:6]
g_b = obs[:, 6:9]
v_b, a_b, g_b = obs[:, 0:3], obs[:, 3:6], obs[:, 6:9]
for i in range(self.lattice_space.vertical_num - 1, -1, -1):
for j in range(self.lattice_space.horizon_num - 1, -1, -1):
Rbp = self.lattice_primitive.getRotation(id)
v_p = np.dot(Rbp.T, v_b.T).T
a_p = np.dot(Rbp.T, a_b.T).T
g_p = np.dot(Rbp.T, g_b.T).T
obs_return[:, i, j, 0:3] = v_p
obs_return[:, i, j, 3:6] = a_p
obs_return[:, i, j, 6:9] = g_p
# obs_return[:, i, j, 0:6] = self.normalize_obs(obs_return[:, i, j, 0:6])
obs_return[:, 0:3, i, j] = np.dot(v_b, Rbp) # v_p
obs_return[:, 3:6, i, j] = np.dot(a_b, Rbp) # a_p
obs_return[:, 6:9, i, j] = np.dot(g_b, Rbp) # g_p
# obs_return[:, 0:6, i, j] = self.normalize_obs(obs_return[:, 0:6, i, j])
id = id + 1
obs_return = np.transpose(obs_return, [0, 3, 1, 2])
return th.from_numpy(obs_return)
def unnormalize_obs(self, vel_acc_norm):
@@ -421,7 +413,7 @@ class YopoAlgorithm:
self._last_obs = self.env.reset()
self._last_depth = self.env.getDepthImage()
self._last_goal = np.zeros([self.env.num_envs, 3], dtype=np.float32)
for i in range(0, self.env.num_envs):
for i in range(self.env.num_envs):
self._last_goal[i] = self.get_random_goal(self._last_obs[i])
self._map_id = np.zeros((self.env.num_envs, 1), dtype=np.float32)
@@ -503,7 +495,7 @@ class YopoAlgorithm:
self.env.setMapID(-np.ones((self.env.num_envs, 1)))
self._last_obs = self.env.reset()
self._last_depth = self.env.getDepthImage()
for i in range(0, self.env.num_envs):
for i in range(self.env.num_envs):
self._last_goal[i] = self.get_random_goal(self._last_obs[i])
def _convert_train_freq(self) -> None: