import park
import agent_impl # implemented by user
env = park.make('congestion_control')
# the run script will start the real system
# and periodically invoke agent.get_action
agent = Agent(env.observation_space, env.action_space)
env.run(agent)The agent_impl.py should implement
class Agent(object):
def __init__(self, state_space, action_space, *args, **kwargs):
self.state_space = state_space
self.action_space = action_space
def get_action(self, obs, prev_reward, prev_done, prev_info):
act = self.action_space.sample()
# implement real action logic here
return actSimilar to OpenAI Gym interface.
import park
env = park.make('load_balance')
obs = env.reset()
done = False
while not done:
# act = agent.get_action(obs)
act = env.action_space.sample()
obs, reward, done, info = env.step(act)| Environment | env_id | Committers |
|---|---|---|
| Adaptive video streaming | abr, abr_sim | Hongzi Mao, Akshay Narayan |
| Spark cluster job scheduling | spark, spark_sim | Hongzi Mao, Malte Schwarzkopf |
| SQL database query optimization | query_optimizer | Parimarjan Negi |
| Network congestion control | congestion_control | Akshay Narayan, Frank Cangialosi |
| Network active queue management | aqm | Mehrdad Khani, Songtao He |
| Tensorflow device placement | tf_placement, tf_placement_sim | Ravichandra Addanki |
| Circuit design | circuit_design | Hanrui Wang, Jiacheng Yang |
| CDN memory caching | cache | Haonan Wang, Wei-Hung Weng |
| Multi-dim database indexing | multi_dim_index | Vikram Nathan |
| Account region assignment | region_assignment | Ryan Marcus |
| Server load balancing | load_balance | Hongzi Mao |
| Switch scheduling | switch_scheduling | Ravichandra Addanki, Hongzi Mao |
Note: to use argparse that is compatiable with park parameters, add parameters using
from park.param import parser
parser.add_argument('--new_parameter')
config = parser.parse_args()
print(config.new_parameter)