Getting Started in Python

#pip3 install https://github.com/IBM/ushiriki-policy-engine-library --user --upgrade


import numpy as np
from sys import exit, exc_info, argv
import random
from ushiriki_policy_engine_library.NeurIPS19ChallengeEnvironment import ChallengeEnvironment
from ushiriki_policy_engine_library.EvaluateSubmission import EvaluateAugmentedChallengeSubmission,  EvaluateChallengeSubmission 

class ChallengeEnvironment1(ChallengeEnvironment):
    def __init__(self):

# Please replace the Agent userID code with the code you received after joining the competition.
# userID="REPLACE_WITH_ID_RECEIVED"

        ChallengeEnvironment.__init__(self,baseuri="http://api.compete.hexagon-ml.com", userID="REPLACE_WITH_ID_RECEIVED" , experimentCount=2000, token="e4e2db39-5daa-48e5-84b4-2460d2844a10" )# for the demo proxy server
class CustomAgent:
    def __init__(self, environment):
        self.environment = environment

    def generate(self):
        best_policy = None
        best_reward = -float('Inf')
        candidates = []
        try:
            # Agents should make use of 20 episodes in each training run, if making sequential decisions
            for i in range(20):
                self.environment.reset()
                policy = {}
                for j in range(self.environment.policyDimension): #episode length
                    policy[str(j+1)]=[random.random(),random.random()]
                candidates.append(policy)
               
            rewards = self.environment.evaluatePolicy(candidates)
            best_policy = candidates[np.argmax(rewards)]
            best_reward = rewards[np.argmax(rewards)]
       
        except (KeyboardInterrupt, SystemExit):
            print(exc_info())       
        return best_policy, best_reward
eval = EvaluateAugmentedChallengeSubmission(ChallengeEnvironment1, CustomAgent, "test_stop.csv")

FAQ

Evaluation

Code must be submitted through the competition platform by the deadline 1200 noon PST Friday 13^th December 2019. The top 3 participants on the Leaderboard at this time have the opportunity to present (5 mins) at the Machine Learning Competitions workshop. All participants may attend and are invited to contribute towards Workshop Proceedings.

Using your NeurIPS Registration ID is required to confirm registration and participation in the live Challenge.

If the winners wish to publish results we request that the Competition be appropriately referenced and code publicly available under an OSI-approved license such as, for instance, Apache 2.0, MIT or BSD-like license.

Rules

Announcements:

To receive announcements and be informed of any change in rules, the participants must provide a valid email to the challenge platform along with their NeurIPS registration ID.

Conditions of participation:

Participation requires complying with the rules of the Competition. Prize eligibility is restricted by US government export regulations. The organizers, sponsors, their students, close family members (parents, sibling, spouse or children) and household members, as well as any person having had access to the truth values or to any information about the data or the Competition design giving him (or her) an unfair advantage are excluded from participation. A disqualified person may submit one or several entries in the Competition and request to have them evaluated, provided that they notify the organizers of their conflict of interest. If a disqualified person submits an entry, this entry will not be part of the final ranking and does not qualify for prizes. The participants should be aware that the organizers reserve the right to evaluate for scientific purposes any entry made in the challenge, whether or not it qualifies for prizes.

Dissemination:

All participants will be invited to attend a workshop organized in conjunction with NeurIPS and contribute to the proceedings.

Registration:

The participants must register to the platform and provide a valid email address along with Conference Regisration ID. Team acknowledgement or submission will not be supported for this competition due to it’s live, on-site nature.

Anonymity:

The participants who do not present their results at the workshop can elect to remain anonymous by using a pseudonym. Their results will be published on the leaderboard under that pseudonym, and their real name will remain confidential. However, the participants must disclose their real identity to the organizers to receive further contact and acknowledgement in published material. 

Submission Expectation:

• Your submission is based on 10 instantiations of the environment object

• Each environment has a limit of 2000 evaluations, with an episode requiring 5 evaluations

• Your submission should include submission of a learnt policy and it's evaluation, in the provided .csv format

• Best practices and expectations should refer to the example and tutorial code

Leaderboard

Data License