def update(self, algorithm, clock):
'''Get an updated value for var'''
if (util.in_eval_lab_mode()) or self._updater_name == 'no_decay':
return self.end_val
step = clock.get()
val = self._updater(self.start_val, self.end_val, self.start_step,
self.end_step, step)
return val
def try_scale_reward(cls, reward):
'''Env class to scale reward'''
if util.in_eval_lab_mode(): # only trigger on training
return reward
if cls.reward_scale is not None:
if cls.sign_reward:
reward = np.sign(reward)
else:
reward *= cls.reward_scale
return reward
def update(self, state, action, reward, next_state, done):
'''Update per timestep after env transitions, e.g. memory, algorithm, update agent params, train net'''
self.body.update(state, action, reward, next_state, done)
if util.in_eval_lab_mode(): # eval does not update agent for training
return
self.body.memory.update(state, action, reward, next_state, done)
loss = self.algorithm.train()
if not np.isnan(loss): # set for log_summary()
self.body.loss = loss
explore_var = self.algorithm.update()
return loss, explore_var
def to_ckpt(self, env, mode='eval'):
'''Check with clock whether to run log/eval ckpt: at the start, save_freq, and the end'''
if mode == 'eval' and util.in_eval_lab_mode(
): # avoid double-eval: eval-ckpt in eval mode
return False
clock = env.clock
frame = clock.get()
frequency = env.eval_frequency if mode == 'eval' else env.log_frequency
to_ckpt = util.frame_mod(frame, frequency,
env.num_envs) or frame == clock.max_frame
return to_ckpt
def save(self, ckpt=None):
'''Save agent'''
if util.in_eval_lab_mode(): # eval does not save new models
return
self.algorithm.save(ckpt=ckpt)