def _setup(self, conditions):
""" Tells the agent, if the environment is discrete or continuous and the
number/dimensionalty of states and actions. This function is called
just before the first state is integrated.
"""
Agent._setup(self, conditions)
# direct learning agents require continuous states/actions
if self.conditions['discreteStates'] or self.conditions['discreteActions']:
raise AgentException('DirectAgent expects continuous states and actions. Use adapter or a different environment.')
if not self.conditions['episodic']:
raise AgentException('DirectAgent expects an episodic environment. Use adapter or different environment.')
self.controller = self.faClass(self.conditions['stateDim'], self.conditions['actionDim'])
def __init__(self, faClass=Linear):
Agent.__init__(self)
self.faClass = faClass
