def calculate_sum_of_rewards(self, obs, candidate_action_sequences,
model: FFModel):
"""
:param obs: numpy array with the *current observation*. Shape [D_obs]
:param candidate_action_sequences: numpy array with the candidate action
sequences. Shape [N, H, D_action] where
- N is the number of action sequences considered
- H is the horizon
- D_action is the action of the dimension
:param model: The current dynamics model.
:return: numpy array with the sum of rewards for each action sequence.
The array should have shape [N].
# You should sum across `self.horizon` time step.
# Hint: you should use model.get_prediction and you shouldn't need
# to import pytorch in this file.
# Hint: Remember that the model can process observations and actions
# in batch, which can be much faster than looping through each
# action sequence.
"""
N = candidate_action_sequences.shape[0]
H = candidate_action_sequences.shape[1]
# For each candidate action sequence, predict a sequence of
# states for each dynamics model in your ensemble.
predicted_obs = np.zeros(shape=(N, H, obs.shape[0]))
predicted_obs_after_step_i = None
for step_i in range(H): # iterate in one step in horizon H
# x=array([1, 2]); np.repeat(x[np.newaxis,:],3,axis=0)--> array([[1, 2],[1, 2],[1, 2]])
if predicted_obs_after_step_i is None:
obs_batch = np.repeat(obs[np.newaxis, :], N, axis=0)
else:
obs_batch = predicted_obs_after_step_i
action_batch = candidate_action_sequences[:, step_i, :]
assert action_batch.shape[0] == obs_batch.shape[0]
predicted_obs_after_step_i = model.get_prediction(
obs_batch, action_batch, self.data_statistics)
assert predicted_obs[:,
step_i, :].shape == predicted_obs_after_step_i.shape
predicted_obs[:, step_i, :] = predicted_obs_after_step_i
# Once you have a sequence of predicted states from each model in
# your ensemble, calculate the sum of rewards for each sequence
# using `self.env.get_reward(predicted_obs)`
sum_of_rewards = np.zeros(shape=N)
for action_sequence_i in range(N):
observations = predicted_obs[action_sequence_i]
actions = candidate_action_sequences[action_sequence_i]
# TODO check whether `done` needs to be used?
r_total_list, _ = self.env.get_reward(
observations,
actions) # actually `actions` is not used in `get_reward`!!
sum_of_rewards[action_sequence_i] = sum(r_total_list)
return sum_of_rewards
def calculate_sum_of_rewards(
self,
obs: np.ndarray,
candidate_action_sequences: np.ndarray,
model: FFModel,
):
"""
:param obs: numpy array with the current observation. Shape [D_obs]
:param candidate_action_sequences: numpy array with the candidate action
sequences. Shape [N, H, D_action] where
- N is the number of action sequences considered
- H is the horizon
- D_action is the action of the dimension
:param model: The current dynamics model.
:return: numpy array with the sum of rewards for each action sequence.
The array should have shape [N].
"""
# For each candidate action sequence, predict a sequence of
# states for each dynamics model in your ensemble.
# Once you have a sequence of predicted states from each model in
# your ensemble, calculate the sum of rewards for each sequence
# using `self.env.get_reward(predicted_obs)`
# You should sum across `self.horizon` time step.
# Hint: you should use model.get_prediction and you shouldn't need
# to import pytorch in this file.
# Hint: Remember that the model can process observations and actions
# in batch, which can be much faster than looping through each
# action sequence.
N, H, _ = candidate_action_sequences.shape
pred_obs = np.zeros((N, H, self.ob_dim))
pred_obs[:, 0] = np.tile(obs[None, :], (N, 1))
rewards = np.zeros((N, H))
for t in range(H):
rewards[:, t], _ = self.env.get_reward(
pred_obs[:, t], candidate_action_sequences[:, t])
if t < H - 1:
pred_obs[:, t + 1] = model.get_prediction(
pred_obs[:, t],
candidate_action_sequences[:, t],
self.data_statistics,
)
sum_of_rewards = rewards.sum(axis=1)
assert sum_of_rewards.shape == (N, )
return sum_of_rewards
def calculate_sum_of_rewards_new(self, obs, candidate_action_sequences,
model: FFModel):
"""
:param obs: numpy array with the *current observation*. Shape [D_obs]
:param candidate_action_sequences: numpy array with the candidate action
sequences. Shape [N, H, D_action] where
- N is the number of action sequences considered
- H is the horizon
- D_action is the action of the dimension
:param model: The current dynamics model.
:return: numpy array with the sum of rewards for each action sequence.
The array should have shape [N].
# You should sum across `self.horizon` time step.
# Hint: you should use model.get_prediction and you shouldn't need
# to import pytorch in this file.
# Hint: Remember that the model can process observations and actions
# in batch, which can be much faster than looping through each
# action sequence.
"""
N = candidate_action_sequences.shape[0]
assert self.N == N
H = candidate_action_sequences.shape[1]
sum_of_rewards = np.zeros(shape=N)
# For each candidate action sequence, predict a sequence of states for each dynamics model in your ensemble.
# Once you have a sequence of predicted states from each model in your ensemble, calculate the sum of rewards for each sequence using `self.env.get_reward(predicted_obs)`
obs = np.repeat(obs[np.newaxis, :], N, axis=0)
for t in range(H):
obs_predicted = model.get_prediction(
obs, candidate_action_sequences[:, t, :], self.data_statistics)
rewards, dones = self.env.get_reward(
obs_predicted,
np.zeros(shape=1)) # reward of coming to the next state
sum_of_rewards += rewards # use and only use one obs to determine reward. can also work in batch mode
return sum_of_rewards
