adding ai_economist for modding

2023-01-12 16:41:38 +01:00
parent 0479a4f6a4
commit f177f8f0ba
85 changed files with 19373 additions and 2 deletions
@@ -0,0 +1,134 @@
+# Copyright (c) 2021, salesforce.com, inc.
+# All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# For full license text, see the LICENSE file in the repo root
+# or https://opensource.org/licenses/BSD-3-Clause
+
+import numpy as np
+
+from ai_economist.foundation.base.base_component import (
+    BaseComponent,
+    component_registry,
+)
+
+
+@component_registry.add
+class SimpleLabor(BaseComponent):
+    """
+    Allows Agents to select a level of labor, which earns income based on skill.
+
+    Labor is "simple" because this simplifies labor to a choice along a 1D axis. More
+    concretely, this component adds 100 labor actions, each representing a choice of
+    how many hours to work, e.g. action 50 represents doing 50 hours of work; each
+    Agent earns income proportional to the product of its labor amount (representing
+    hours worked) and its skill (representing wage), with higher skill and higher labor
+    yielding higher income.
+
+    This component is intended to be used with the 'PeriodicBracketTax' component and
+    the 'one-step-economy' scenario.
+
+    Args:
+        mask_first_step (bool): Defaults to True. If True, masks all non-0 labor
+            actions on the first step of the environment. When combined with the
+            intended component/scenario, the first env step is used to set taxes
+            (via the 'redistribution' component) and the second step is used to
+            select labor (via this component).
+        payment_max_skill_multiplier (float): When determining the skill level of
+            each Agent, sampled skills are clipped to this maximum value.
+    """
+
+    name = "SimpleLabor"
+    required_entities = ["Coin"]
+    agent_subclasses = ["BasicMobileAgent"]
+
+    def __init__(
+        self,
+        *base_component_args,
+        mask_first_step=True,
+        payment_max_skill_multiplier=3,
+        pareto_param=4.0,
+        **base_component_kwargs
+    ):
+        super().__init__(*base_component_args, **base_component_kwargs)
+
+        # This defines the size of the action space (the max # hours an agent can work).
+        self.num_labor_hours = 100  # max 100 hours
+
+        assert isinstance(mask_first_step, bool)
+        self.mask_first_step = mask_first_step
+
+        self.is_first_step = True
+        self.common_mask_on = {
+            agent.idx: np.ones((self.num_labor_hours,)) for agent in self.world.agents
+        }
+        self.common_mask_off = {
+            agent.idx: np.zeros((self.num_labor_hours,)) for agent in self.world.agents
+        }
+
+        # Skill distribution
+        self.pareto_param = float(pareto_param)
+        assert self.pareto_param > 0
+        self.payment_max_skill_multiplier = float(payment_max_skill_multiplier)
+        pmsm = self.payment_max_skill_multiplier
+        num_agents = len(self.world.agents)
+        # Generate a batch (1000) of num_agents (sorted/clipped) Pareto samples.
+        pareto_samples = np.random.pareto(4, size=(1000, num_agents))
+        clipped_skills = np.minimum(pmsm, (pmsm - 1) * pareto_samples + 1)
+        sorted_clipped_skills = np.sort(clipped_skills, axis=1)
+        # The skill level of the i-th skill-ranked agent is the average of the
+        # i-th ranked samples throughout the batch.
+        self.skills = sorted_clipped_skills.mean(axis=0)
+
+    def get_additional_state_fields(self, agent_cls_name):
+        if agent_cls_name == "BasicMobileAgent":
+            return {"skill": 0, "production": 0}
+        return {}
+
+    def additional_reset_steps(self):
+        self.is_first_step = True
+        for agent in self.world.agents:
+            agent.state["skill"] = self.skills[agent.idx]
+
+    def get_n_actions(self, agent_cls_name):
+        if agent_cls_name == "BasicMobileAgent":
+            return self.num_labor_hours
+        return None
+
+    def generate_masks(self, completions=0):
+        if self.is_first_step:
+            self.is_first_step = False
+            if self.mask_first_step:
+                return self.common_mask_off
+
+        return self.common_mask_on
+
+    def component_step(self):
+
+        for agent in self.world.get_random_order_agents():
+
+            action = agent.get_component_action(self.name)
+
+            if action == 0:  # NO-OP.
+                # Agent is not interacting with this component.
+                continue
+
+            if 1 <= action <= self.num_labor_hours:  # set reopening phase
+
+                hours_worked = action  # NO-OP is 0 hours.
+                agent.state["endogenous"]["Labor"] = hours_worked
+
+                payoff = hours_worked * agent.state["skill"]
+                agent.state["production"] += payoff
+                agent.inventory["Coin"] += payoff
+
+            else:
+                # If action > num_labor_hours, this is an error.
+                raise ValueError
+
+    def generate_observations(self):
+        obs_dict = dict()
+        for agent in self.world.agents:
+            obs_dict[str(agent.idx)] = {
+                "skill": agent.state["skill"] / self.payment_max_skill_multiplier
+            }
+        return obs_dict