world.py
def __update_world(self, tau, t):
        """This private method performs a signel time step update of the
        whole world. Here we update the hidden state(s) of the environment,
        the perceptual and planning states of the agent, and in parallel we
        generate observations and actions.
        """

        if t==0:
            self.environment.set_initial_states(tau)
            response = None
        else:
            response = self.actions[tau, t-1]
            self.environment.update_hidden_states(tau, t, response)

        self.observations[tau, t] = \
            self.environment.generate_observations(tau, t)

        if t>0:
            self.rewards[tau, t] = self.environment.generate_rewards(tau, t)

        observation = self.observations[tau, t]

        reward = self.rewards[tau, t]

        self.agent.update_beliefs(tau, t, observation, reward, response)


        if t < self.T-1:
            self.actions[tau, t] = self.agent.generate_response(tau, t)
        else:
            self.actions[tau, t] = -1
This private method performs a signel time step update of the
        whole world. Here we update the hidden state(s) of the environment,
        the perceptual and planning states of the agent, and in parallel we
        generate observations and actions.
        
This private method performs a signel time step update of the
whole world. Here we update the hidden state(s) of the environment,
the perceptual and planning states of the agent, and in parallel we
generate observations and actions.
This private method performs a signel time step update of the whole world . Here we update the hidden state ( s ) of the environment the perceptual and planning states of the agent and in parallel we generate observations and actions .
def __update_world(self, tau, t):
        if t==0:
            self.environment.set_initial_states(tau)
            response = None
        else:
            response = self.actions[tau, t-1]
            self.environment.update_hidden_states(tau, t, response)
        self.observations[tau, t] = \
            self.environment.generate_observations(tau, t)
        if t>0:
            self.rewards[tau, t] = self.environment.generate_rewards(tau, t)
        observation = self.observations[tau, t]
        reward = self.rewards[tau, t]
        self.agent.update_beliefs(tau, t, observation, reward, response)
        if t < self.T-1:
            self.actions[tau, t] = self.agent.generate_response(tau, t)
        else:
            self.actions[tau, t] = -1
def __update_world ( self , tau , t ) : if t == 0 : self . environment . set_initial_states ( tau ) response = None else : response = self . actions [ tau , t - 1 ] self . environment . update_hidden_states ( tau , t , response ) self . observations [ tau , t ] = self . environment . generate_observations ( tau , t ) if t > 0 : self . rewards [ tau , t ] = self . environment . generate_rewards ( tau , t ) observation = self . observations [ tau , t ] reward = self . rewards [ tau , t ] self . agent . update_beliefs ( tau , t , observation , reward , response ) if t < self . T - 1 : self . actions [ tau , t ] = self . agent . generate_response ( tau , t ) else : self . actions [ tau , t ] = - 1
This private method performs a signel time step update of the
whole world.
This private method performs a signel time step update of the whole world .
"""This private method performs a signel time step update of the
        whole world. Here we update the hidden state(s) of the environment,
        the perceptual and planning states of the agent, and in parallel we
        generate observations and actions.
        """
self
tau
t