remove tcmalloc doc (#194)

examples/A3C-Gym/README.md

@@ -20,10 +20,9 @@ because the bottleneck in this implementation is not computation but data.
 
 Some practicical notes:
 
-1. On machines without huge memory, enabling tcmalloc may keep training throughput more stable.
-2. Occasionally, processes may not get terminated completely. It is suggested to use `systemd-run` to run any
+1. Occasionally, processes may not get terminated completely. It is suggested to use `systemd-run` to run any
 multiprocess Python program to get a cgroup dedicated for the task.
-3. Training with a significant slower speed (e.g. on CPU) will result in very bad score, probably because of async issues.
+2. Training with a significant slower speed (e.g. on CPU) will result in very bad score, probably because of async issues.
 
 ### To run a pretrained Atari model for 100 episodes:
 

examples/DeepQNetwork/DQN.py

@@ -41,7 +41,6 @@ END_EXPLORATION = 0.1
 
 MEMORY_SIZE = 1e6
 # NOTE: will consume at least 1e6 * 84 * 84 bytes == 6.6G memory.
-# Suggest using tcmalloc to manage memory space better.
 INIT_MEMORY_SIZE = 5e4
 STEPS_PER_EPOCH = 10000
 EVAL_EPISODE = 50