def expectedStep(self, s, a):
# Returns k possible outcomes
# p: k-by-1 probability of each transition
# r: k-by-1 rewards
# ns: k-by-|s| next state
# t: k-by-1 terminal values
# pa: k-by-?? possible actions for each next state
actions = self.possibleActions(s)
k = len(actions)
# Make Probabilities
intended_action_index = findElemArray1D(a, actions)
p = np.ones((k, 1)) * self.NOISE / (k * 1.)
p[intended_action_index, 0] += 1 - self.NOISE
# Make next states
ns = np.tile(s, (k, 1)).astype(int)
actions = self.ACTIONS[actions]
ns += actions
# Make next possible actions
pa = np.array([self.possibleActions(sn) for sn in ns])
# Make rewards
r = np.ones((k, 1)) * self.STEP_REWARD
goal = self.map[ns[:, 0], ns[:, 1]] == self.GOAL
pit = self.map[ns[:, 0], ns[:, 1]] == self.PIT
r[goal] = self.GOAL_REWARD
r[pit] = self.PIT_REWARD
# Make terminals
t = np.zeros((k, 1), bool)
t[goal] = True
t[pit] = True
return p, r, ns, t, pa
def top(self, A, s):
# returns the block on top of block A. Return [] if nothing is on top
# of A
on_A = findElemArray1D(A, s)
on_A = np.setdiff1d(on_A,
[A]) # S[i] = i is the key for i is on table.
return on_A
def expectedStep(self, s, a):
# Returns k possible outcomes
# p: k-by-1 probability of each transition
# r: k-by-1 rewards
# ns: k-by-|s| next state
# t: k-by-1 terminal values
# pa: k-by-?? possible actions for each next state
actions = self.possibleActions(s)
k = len(actions)
# Make Probabilities
intended_action_index = findElemArray1D(a, actions)
p = np.ones((k, 1)) * self.NOISE / (k * 1.)
p[intended_action_index, 0] += 1 - self.NOISE
# Make next states
ns = np.tile(s, (k, 1)).astype(int)
actions = self.ACTIONS[actions]
ns += actions
# Make next possible actions
pa = np.array([self.possibleActions(sn) for sn in ns])
# Make rewards
r = np.ones((k, 1)) * self.STEP_REWARD
goal = self.map[ns[:, 0], ns[:, 1]] == self.GOAL
pit = self.map[ns[:, 0], ns[:, 1]] == self.PIT
r[goal] = self.GOAL_REWARD
r[pit] = self.PIT_REWARD
# Make terminals
t = np.zeros((k, 1), bool)
t[goal] = True
t[pit] = True
return p, r, ns, t, pa
def bestActions(self, s, terminal, p_actions, phi_s=None):
"""
Returns a list of the best actions at a given state.
If *phi_s* [the feature vector at state *s*] is given, it is used to
speed up code by preventing re-computation within this function.
See :py:meth:`~rlpy.Representations.Representation.Representation.bestAction`
:param s: The given state
:param terminal: Whether or not the state *s* is a terminal one.
:param phi_s: (optional) the feature vector at state (s).
:return: A list of the best actions at the given state.
"""
Qs = self.Qs(s, terminal, phi_s)
Qs = Qs[p_actions]
# Find the index of best actions
ind = findElemArray1D(Qs, Qs.max())
return np.array(p_actions)[ind]
def bestActions(self, s, terminal, p_actions, phi_s=None):
"""
Returns a list of the best actions at a given state.
If *phi_s* [the feature vector at state *s*] is given, it is used to
speed up code by preventing re-computation within this function.
See :py:meth:`~rlpy.Representations.Representation.Representation.bestAction`
:param s: The given state
:param terminal: Whether or not the state *s* is a terminal one.
:param phi_s: (optional) the feature vector at state (s).
:return: A list of the best actions at the given state.
"""
Qs = self.Qs(s, terminal, phi_s)
Qs = Qs[p_actions]
# Find the index of best actions
ind = findElemArray1D(Qs, Qs.max())
return np.array(p_actions)[ind]
def top(self, A, s):
# returns the block on top of block A. Return [] if nothing is on top
# of A
on_A = findElemArray1D(A, s)
on_A = np.setdiff1d(on_A, [A]) # S[i] = i is the key for i is on table.
return on_A
