def update(self, _, a, r, s1):
if not self._stop:
a = double_to_single_precision(a)
r = double_to_single_precision(r)
s1 = double_to_single_precision(np.array(s1))
d = double_to_single_precision(1.0)
timestep = dm_env.transition(reward=r, observation=s1, discount=d)
self.agent.observe(a, timestep)
self.agent.update()
# Log values.
values = {
'step': self._obs_counter,
'action': a,
'reward': r,
}
self._logger.write(values)
def update(self, _, a, r, s1):
if not self._stop:
a = double_to_single_precision(np.array(a))
r = double_to_single_precision(r)
s1 = double_to_single_precision(np.array(s1))
d = double_to_single_precision(1.0)
timestep = dm_env.transition(reward=r, observation=s1, discount=d)
self.agent.observe(a, timestep)
self.agent.update()
# Log values.
values = {
'step': self._obs_counter,
'reward': r,
}
for i in range(self._params.num_phases):
values[f"action_p{i}"] = a[i]
self._logger.write(values)
def act(self, s):
s = double_to_single_precision(np.array(s))
# Make first observation.
if self._obs_counter == 0:
t_1 = dm_env.restart(s)
self.agent.observe_first(t_1)
# Select action.
if self._stop:
action = self.agent.deterministic_action(s)
else:
action = self.agent.select_action(s)
self._obs_counter += 1
return int(action)