Usage – Configuration

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

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 for the training-task. Each RL-algorithm requires different parameters, listed in the sections below. The sections are named after the respective class names of the agents in the framework.

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

`QLearningAgent`

The QLearningAgent is an algorithm implemented by the project team. It requires the rl_config to contain the following parameters:

Key	Value	Description
`gamma`	`float`
`batch_size`	`int`
`replay_size`	`int`
`learning_rate`	`float`
`sync_target_frames`	`int`
`replay_start_size`	`int`
`epsilon_decay_last_frame`	`int`	The last episode in which the epsilon will be decreased.
`epsilon_start`	`float`	The starting value of the epsilon. Must be between 0 and 1 and bigger than `epsilon_final`.
`epsilon_final`	`float`	The end value of the epsilon. Must be between 0 and 1 and smaller than `epsilon_start`.

`ActorCriticAgent`

There are two ActorCriticAgents that were implemented by the project team: The DiscreteActorCriticAgent and the ContinuousActorCriticAgent. Both of them require their rl_config to contain the following parameters: