add_header ( 'Authorization', '***' ) # Make the HTTP request. # Replace *** with your API key, from your user account on the CKAN site # that you're creating the dataset on. Request ( '' ) # Creating a dataset requires an authorization header. dumps ( dataset_dict )) # We'll use the package_create function to create a new dataset. dataset_dict = # Use the json module to dump the dictionary to a string for posting. #!/usr/bin/env python import urllib2 import urllib import json import pprint # Put the details of the dataset we're going to create into a dict. Get an activity stream of recently changed datasets on a site: Search for packages or resources matching a query:Ĭreate, update and delete datasets, resources and other objects Get a full JSON representation of a dataset, resource or other object: Get JSON-formatted lists of a site’s datasets, groups or other CKAN objects: For example, using the CKAN API your app can: (everything you can do with the web interface and more) can be used by externalĬode that calls the CKAN API. All of a CKAN website’s core functionality Interacts with CKAN sites and their data.ĬKAN’s Action API is a powerful, RPC-style API that exposes all of CKAN’sĬore features to API clients. After every action, the agent also receives a reward \\(r\\) from the environment.This section documents CKAN’s API, for developers who want to write code that This is a function from states \\(s\\) to an action \\(a\\), or more generally to a distribution over the possible actions. Briefly, an agent interacts with the environment based on its policy \\(\pi(a \mid s)\\). Note that you can select any cell and change its reward with the *Cell reward* slider.Īn interested reader should refer to **Richard Sutton's Free Online Book on Reinforcement Learning**, in this particular case (). The color of the cells (initially all white) shows the current estimate of the Value (discounted reward) of that state, with the current policy. My favorite part is letting Value iteration converge, then change the cell rewards and watch the policy adjust. In other words, this is a deterministic, finite Markov Decision Process (MDP) and as always the goal is to find an agent policy (shown here by arrows) that maximizes the future discounted reward. The state with 1.0 reward is the goal state and resets the agent back to start. **Rewards**: The agent receives 1 reward when it is in the center square (the one that shows R 1.0), and -1 reward in a few states (R -1.0 is shown for these). **Environment Dynamics**: GridWorld is deterministic, leading to the same new state given each state and action **Actions**: The agent can choose from up to 4 actions to move around. The gray cells are walls and cannot be moved to. **State space**: GridWorld has 10x10 = 100 distinct states. This is a toy environment called **Gridworld** that is often used as a toy model in the Reinforcement Learning literature.
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