
import opensim as osim

from osim.redis.client import Client
from osim.env import *
import numpy as np
import argparse
import os

"""
Please ensure that `visualize=False`, else there might be unexpected errors in your submission
"""
env = RunEnv(visualize=False)
client = Client()

# Create environment
observation = client.env_create()

"""
The grader runs N simulations of at most 1000 steps each. We stop after the last one
A new simulation start when `clinet.env_step` returns `done==True`
and all the simulatiosn end when the subsequent `client.env_reset()` returns a False
"""
while True:
    _action = env.action_space.sample().tolist()
    [observation, reward, done, info] = client.env_step(_action)
    print(observation)
    if done:
        observation = client.env_reset()
        if not observation:
            break

client.submit()


import opensim as osim

from osim.redis.client import Client
from osim.env import *
import numpy as np
import argparse
import os

"""
NOTE: For testing your submission scripts, you first need to ensure that redis-server is running in the background
and you can locally run the grading service by running this script : https://github.com/crowdAI/osim-rl/blob/master/osim/redis/service.py

The client and the grading service communicate with each other by pointing to the same redis server.
"""

"""
Please ensure that `visualize=False`, else there might be unexpected errors in your submission
"""
env = RunEnv(visualize=False)
client = Client()

# Create environment
observation = client.env_create()

"""
The grader runs N simulations of at most 1000 steps each. We stop after the last one
A new simulation start when `clinet.env_step` returns `done==True`
and all the simulatiosn end when the subsequent `client.env_reset()` returns a False
"""
while True:
    _action = env.action_space.sample().tolist()
    [observation, reward, done, info] = client.env_step(_action)
    print(observation)
    if done:
        observation = client.env_reset()
        if not observation:
            break

client.submit()

Add a little bit more documentation for round2submission script
Add a little bit more documentation for round2submission script

  
  import opensim as osim
  
  from osim.redis.client import Client
  from osim.env import *
  import numpy as np
  import argparse
  import os
+ 
+ """
+ NOTE: For testing your submission scripts, you first need to ensure that redis-server is running in the background
+ and you can locally run the grading service by running this script : https://github.com/crowdAI/osim-rl/blob/master/osim/redis/service.py
+ 
+ The client and the grading service communicate with each other by pointing to the same redis server.
+ """
  
  """
  Please ensure that `visualize=False`, else there might be unexpected errors in your submission
  """
  env = RunEnv(visualize=False)
  client = Client()
  
  # Create environment
  observation = client.env_create()
  
  """
  The grader runs N simulations of at most 1000 steps each. We stop after the last one
  A new simulation start when `clinet.env_step` returns `done==True`
  and all the simulatiosn end when the subsequent `client.env_reset()` returns a False
  """
  while True:
      _action = env.action_space.sample().tolist()
      [observation, reward, done, info] = client.env_step(_action)
      print(observation)
      if done:
          observation = client.env_reset()
          if not observation:
              break
  
  client.submit()
  
Add a little bit more documentation for round2submission script
## Code Before:

import opensim as osim

from osim.redis.client import Client
from osim.env import *
import numpy as np
import argparse
import os

"""
Please ensure that `visualize=False`, else there might be unexpected errors in your submission
"""
env = RunEnv(visualize=False)
client = Client()

# Create environment
observation = client.env_create()

"""
The grader runs N simulations of at most 1000 steps each. We stop after the last one
A new simulation start when `clinet.env_step` returns `done==True`
and all the simulatiosn end when the subsequent `client.env_reset()` returns a False
"""
while True:
    _action = env.action_space.sample().tolist()
    [observation, reward, done, info] = client.env_step(_action)
    print(observation)
    if done:
        observation = client.env_reset()
        if not observation:
            break

client.submit()

## Instruction:
Add a little bit more documentation for round2submission script
## Code After:

import opensim as osim

from osim.redis.client import Client
from osim.env import *
import numpy as np
import argparse
import os

"""
NOTE: For testing your submission scripts, you first need to ensure that redis-server is running in the background
and you can locally run the grading service by running this script : https://github.com/crowdAI/osim-rl/blob/master/osim/redis/service.py

The client and the grading service communicate with each other by pointing to the same redis server.
"""

"""
Please ensure that `visualize=False`, else there might be unexpected errors in your submission
"""
env = RunEnv(visualize=False)
client = Client()

# Create environment
observation = client.env_create()

"""
The grader runs N simulations of at most 1000 steps each. We stop after the last one
A new simulation start when `clinet.env_step` returns `done==True`
and all the simulatiosn end when the subsequent `client.env_reset()` returns a False
"""
while True:
    _action = env.action_space.sample().tolist()
    [observation, reward, done, info] = client.env_step(_action)
    print(observation)
    if done:
        observation = client.env_reset()
        if not observation:
            break

client.submit()
