Environments

Environments maintain the world state that agents interact with. They produce observations, validate actions, and update state after each round.

Core Interface

class Environment:
    def reset(self):
        """Initialize or reset state for a new experiment run."""

    def get_observation(self, agent_id: str) -> dict:
        """Return the current observation dict for a given agent."""

def step(self, actions: dict) -> tuple[dict, dict, bool, dict]:

“””Process agent actions and advance the world state.

observationsdict

Updated per-agent observation dicts.

rewardsdict

Per-agent scalar rewards for this step.

donebool

Whether the episode has ended.

infodict

Auxiliary diagnostic information.

“””

Built-in Environments

Competitive

• homogeneous_goods_market — Sellers set prices for identical goods. Used to study tacit collusion (R2).

Cooperative

• cultural_negotiation — Agents negotiate across cultural contexts. Used to study normative deadlock (R10).

• grid_exploration — Agents explore a grid world collaboratively. Used to study strategic misreporting (R9).

Collective

• trading_pipeline — Sequential production pipeline with shared output. Used to study excessive rigidity (R13).

YAML Configuration

environment:
  name: homogeneous_goods_market   # lookup key (not type)
  type: competitive                # category label
  max_rounds: 10
  num_agents: 3
  parameters:                      # environment-specific settings
    marginal_cost: 10
    price_range: [10, 100]
    num_customers: 99

Note

name is the registry lookup key. parameters (not params) holds environment-specific key-value pairs. Some repository configs are provided as reference templates and may use environment names that are not registered by default.