def evaluate(self, state):
""" Evaluates the policy for the given state """
# If bias is desired, we simply append an additional dimension that
# always takes the value 1
if self.bias:
dimensions = [dimension for dimension in state.dimensions]
biasDimension = Dimension("zzz_bias", "continuous", [[0, 1]])
dimensions.append(biasDimension)
input = State(numpy.hstack((state, [1])), dimensions)
else: # Just create a copy of the state
input = State(state, state.dimensions)
# Scale state dimensions to range (-1, 1)
input.scale(-1, 1)
# Compute the activation (the preference of the policy) for each action
# The last action has always activation 0 (remove redundant
# representations for the same policy)
actionActivations = []
for actionIndex in range(self.numActions - 1):
activation = numpy.dot(
self.weights[self.inputDims * actionIndex:self.inputDims *
(actionIndex + 1)], input)
actionActivations.append(activation)
actionActivations.append(0.0)
# Greedy action selection
selectedAction = max(
zip(actionActivations, range(len(actionActivations))))[1]
return self.actions[selectedAction]
def _jointStateAction(self, state, action):
""" Create a joint state-action pseudo-state """
dimensions = [dimension for dimension in state.dimensions]
actionDimension = copy.deepcopy(
self.actionSpace.getDimensions()[0]) # there is per assert only 1
dimensions.append(actionDimension)
stateAction = State(numpy.hstack((state, action)), dimensions)
stateAction.scale()
return stateAction
def getTile(self, state):
""" Compute the activated tile for the given state_value """
if state in self.stateToTileCache:
return self.stateToTileCache[state]
else:
scaledState = State(state, copy.copy(
state.dimensions)) # avoid side-effects
scaledState.scale(0, 1)
tile = tuple(
numpy.round((numpy.array(scaledState) + self.offset) *
self.tilesPerDimension).astype(numpy.int))
self.stateToTileCache[state] = tile
self.recentStatesOrder.appendleft(state)
if len(self.recentStatesOrder) > 50:
oldestState = self.recentStatesOrder.pop()
self.stateToTileCache.pop(oldestState)
return tile
def evaluate(self, state):
""" Evaluates the policy for the given state """
# If bias is desired, we simply append an additional dimension that
# always takes the value 1
if self.bias:
dimensions = [dimension for dimension in state.dimensions]
biasDimension = Dimension("zzz_bias", "continuous", [[0, 1]])
dimensions.append(biasDimension)
state = State(numpy.hstack((state, [1])), dimensions)
# Scale state dimensions to range (-1, 1)
state.scale(-1, 1)
# Compute the activation (the preference of the policy) for each action
output = []
for outputDimIndex in range(self.numActions):
activation = numpy.dot(
self.weights[self.inputDims * outputDimIndex:self.inputDims *
(outputDimIndex + 1)], state)
output.append(activation)
return output
