Limit impala to single GPU training (#152)

* Limit impala to single GPU training

* refine comment of scheduler

* refine comment

examples/A2C/train.py

@@ -53,7 +53,7 @@ class Learner(object):
 
         if machine_info.is_gpu_available():
             assert get_gpu_count() == 1, 'Only support training in single GPU,\
-                    Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_YOU_WANT_TO_USE]` .'
+                    Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
 
         else:
             cpu_num = os.environ.get('CPU_NUM')

examples/IMPALA/train.py

@@ -22,10 +22,11 @@ import parl
 from atari_model import AtariModel
 from atari_agent import AtariAgent
 from parl.env.atari_wrappers import wrap_deepmind
-from parl.utils import logger, tensorboard
+from parl.utils import logger, tensorboard, get_gpu_count
 from parl.utils.scheduler import PiecewiseScheduler
 from parl.utils.time_stat import TimeStat
 from parl.utils.window_stat import WindowStat
+from parl.utils import machine_info
 
 from actor import Actor
 
@@ -54,6 +55,10 @@ class Learner(object):
         self.agent = AtariAgent(algorithm, obs_shape, act_dim,
                                 self.learn_data_provider)
 
+        if machine_info.is_gpu_available():
+            assert get_gpu_count() == 1, 'Only support training in single GPU,\
+                    Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
+
         self.cache_params = self.agent.get_weights()
         self.params_lock = threading.Lock()
         self.params_updated = False

parl/utils/scheduler.py

@@ -67,7 +67,7 @@ class LinearDecayScheduler(object):
 
     def __init__(self, start_value, max_steps):
         """Linear decay scheduler of hyper parameter.
-
+        Decay value linearly untill 0.
         
         Args:
             start_value (float): start value