Usage – Configuration

This page will introduce the various configuration files used by the different tasks that can be performed within the recommerce-framework.

• environment_config
• market_config
• rl_config

All configuration in the recommerce-framework is done through .json-files. These files are located in the configuration_files folder of the user's datapath (link). The following sections will introduce all types of configuration files, and which parameters can be set through them.

environment_config

The environment_config file contains parameters that are at the highest level of the simulation, among other including the definition of the task that should be performed. Currently, there are three possible tasks, each with their own environment_config file, detailed below. However, the follwing parameters are common to all tasks, and only the agent_monitoring-task requires additional parameters.

Key	Value	Description
task	training, exampleprinter or agent_monitoring	The task that should be performed.
marketplace	A valid recommerce marketplace class as string	The marketplace class that should be used for the simulation.
agents	list of dictionaries	The agents that should be playing on the selected marketplace. See below for the format of the dictionaries.

As mentioned above, the agents key should have a list of dictionaries as its value. Each dictionary should have the following structure:

Key	Value	Description
name	string	The name that should be used to identify the agent.
agent_class	A valid recommerce agent class as string	The class of the agent. Must fit the marketplace class.
argument	string or list	Depending on the chosen agent_class and task, an argument may be needed. For trained RL-agents this is the name of the modelfile that should be used. For FixedPrice-agents this is a list containing the fixed prices that should be used. If no argument is needed, this must be the empty string.

environment_config_agent_monitoring

The agent_monitoring task requires the following parameters in addition to the parameters common to all tasks:

Key	Value	Description
episodes	int	The number of episodes that should be simulated.
plot_interval	int	The interval at which the plots should be updated. Supposed to be removed in the future, see this issue.
separate_markets	bool	If true, the agents will play on different but identical markets. If false, the agents will play on the same market against each other.

market_config

THe market_config contains parameters that influence how the market simulation itself plays out. Circular Economy marketplaces require the following parameters:

Key	Value	Description
max_storage	int	The maximum number of items a vendor can have in storage. This also influences the policies of many rule-based vendors.
episode_length	int	How many steps an episode has.
max_price	int	The maximum price vendors can set for any price channel.
number_of_customers	int	The number of customers that make purchasing decisions each step.
production_price	int	The cost of selling a new product.
storage_cost_per_product	float	The cost for storing one item in inventory for one episode.
opposite_own_state_visibility	bool	Whether or not vendors know the amount of items in storage of their competitors.
common_state_visibility	bool	Whether or not vendors know the amount of items in circulation.
reward_mixed_profit_and_difference	bool	A toggle for the way an agent's reward is calculated. If true, the reward is not only dependent on the agent's own profits, but also the profits of its competitors.

market_config for Linear Markets

In addition to the parameters required by the Circular Economy market, the Linear Markets also require the following parameter:

Key	Value	Description
max_quality	int	The maximum quality a product can be (randomly) assigned at the start of an episode.

TODO: If there are parameters exclusive to Circular Economies, list them here

rl_config

The rl_config file contains parameters that are used to configure Reinforcement Learning agents that are used during a task.

TODO: Find out if the config is used for something else than training