Files
ray/python/ray/rllib/tuned_examples/pong-apex.yaml
T
Eric Liang 882a649f0c [rllib] [docs] Cleanup RLlib API and make docs consistent with upcoming blog post (#1708)
* wip

* more work

* fix apex

* docs

* apex doc

* pool comment

* clean up

* make wrap stack pluggable

* Mon Mar 12 21:45:50 PDT 2018

* clean up comment

* table

* Mon Mar 12 22:51:57 PDT 2018

* Mon Mar 12 22:53:05 PDT 2018

* Mon Mar 12 22:55:03 PDT 2018

* Mon Mar 12 22:56:18 PDT 2018

* Mon Mar 12 22:59:54 PDT 2018

* Update apex_optimizer.py

* Update index.rst

* Update README.rst

* Update README.rst

* comments

* Wed Mar 14 19:01:02 PDT 2018
2018-03-15 15:57:31 -07:00

17 lines
572 B
YAML

# This can be expected to reach 20.8 reward within an hour when using a V100 GPU
# (e.g. p3.2xl instance on AWS, and m4.4xl workers). It also can reach ~21 reward
# within an hour with fewer workers (e.g. 4-8) but less reliably.
pong-apex:
env: PongNoFrameskip-v4
run: APEX
resources:
cpu:
eval: spec.config.num_workers
gpu: 1
config:
force_evaluators_remote: True # set to False if you're running on a single node
target_network_update_freq: 50000
num_workers: 32
lr: .0001
gamma: 0.99