def get_current_optimization_metrics(self,
agents,
isoelastic_eta=0.23,
labor_exponent=2.0,
labor_coefficient=0.1):
"""
Compute optimization metrics based on the current state. Used to compute reward.
Returns:
curr_optimization_metric (dict): A dictionary of {agent.idx: metric}
with an entry for each agent (including the planner) in the env.
"""
curr_optimization_metric = {}
coin_endowments = np.array(
[agent.total_endowment("Coin") for agent in agents])
pretax_incomes = np.array(
[agent.state["production"] for agent in agents])
# Optimization metric for agents:
for agent in agents:
if self.agent_reward_type == "isoelastic_coin_minus_labor":
assert 0.0 <= isoelastic_eta <= 1.0
curr_optimization_metric[
agent.idx] = rewards.isoelastic_coin_minus_labor(
coin_endowment=agent.total_endowment("Coin"),
total_labor=agent.state["endogenous"]["Labor"],
isoelastic_eta=isoelastic_eta,
labor_coefficient=labor_coefficient,
)
elif self.agent_reward_type == "coin_minus_labor_cost":
assert labor_exponent > 1.0
curr_optimization_metric[
agent.idx] = rewards.coin_minus_labor_cost(
coin_endowment=agent.total_endowment("Coin"),
total_labor=agent.state["endogenous"]["Labor"],
labor_exponent=labor_exponent,
labor_coefficient=labor_coefficient,
)
# Optimization metric for the planner:
if self.planner_reward_type == "coin_eq_times_productivity":
curr_optimization_metric[
self.world.planner.idx] = rewards.coin_eq_times_productivity(
coin_endowments=coin_endowments,
equality_weight=1 - self.mixing_weight_gini_vs_coin,
)
elif self.planner_reward_type == "inv_income_weighted_utility":
curr_optimization_metric[
self.world.planner.idx] = rewards.inv_income_weighted_utility(
coin_endowments=pretax_incomes, # coin_endowments,
utilities=np.array([
curr_optimization_metric[agent.idx] for agent in agents
]),
)
else:
print("No valid planner reward selected!")
raise NotImplementedError
return curr_optimization_metric
def scenario_metrics(self):
"""
Allows the scenario to generate metrics (collected along with component metrics
in the 'metrics' property).
To have the scenario add metrics, this function needs to return a dictionary of
{metric_key: value} where 'value' is a scalar (no nesting or lists!)
Here, summarize social metrics, endowments, utilities, and labor cost annealing.
"""
metrics = dict()
coin_endowments = np.array(
[agent.total_endowment("Coin") for agent in self.world.agents]
)
metrics["social/productivity"] = social_metrics.get_productivity(
coin_endowments
)
metrics["social/equality"] = social_metrics.get_equality(coin_endowments)
utilities = np.array(
[self.curr_optimization_metric[agent.idx] for agent in self.world.agents]
)
metrics[
"social_welfare/coin_eq_times_productivity"
] = rewards.coin_eq_times_productivity(
coin_endowments=coin_endowments, equality_weight=1.0
)
metrics[
"social_welfare/inv_income_weighted_coin_endow"
] = rewards.inv_income_weighted_coin_endowments(coin_endowments=coin_endowments)
metrics[
"social_welfare/inv_income_weighted_utility"
] = rewards.inv_income_weighted_utility(
coin_endowments=coin_endowments, utilities=utilities
)
for agent in self.all_agents:
for resource, quantity in agent.inventory.items():
metrics[
"endow/{}/{}".format(agent.idx, resource)
] = agent.total_endowment(resource)
if agent.endogenous is not None:
for resource, quantity in agent.endogenous.items():
metrics["endogenous/{}/{}".format(agent.idx, resource)] = quantity
metrics["util/{}".format(agent.idx)] = self.curr_optimization_metric[
agent.idx
]
# Labor weight
metrics["labor/weighted_cost"] = self.energy_cost * self.energy_weight
metrics["labor/warmup_integrator"] = int(self._auto_warmup_integrator)
return metrics
def get_current_optimization_metrics(self):
"""
Compute optimization metrics based on the current state. Used to compute reward.
Returns:
curr_optimization_metric (dict): A dictionary of {agent.idx: metric}
with an entry for each agent (including the planner) in the env.
"""
curr_optimization_metric = {}
# (for agents)
for agent in self.world.agents:
curr_optimization_metric[
agent.idx] = rewards.isoelastic_coin_minus_labor(
coin_endowment=agent.total_endowment("Coin"),
total_labor=agent.state["endogenous"]["Labor"],
isoelastic_eta=self.isoelastic_eta,
labor_coefficient=self.energy_weight * self.energy_cost,
)
# (for the planner)
if self.planner_reward_type == "coin_eq_times_productivity":
curr_optimization_metric[
self.world.planner.idx] = rewards.coin_eq_times_productivity(
coin_endowments=np.array([
agent.total_endowment("Coin")
for agent in self.world.agents
]),
equality_weight=1 - self.mixing_weight_gini_vs_coin,
)
elif self.planner_reward_type == "inv_income_weighted_coin_endowments":
curr_optimization_metric[
self.world.planner.
idx] = rewards.inv_income_weighted_coin_endowments(
coin_endowments=np.array([
agent.total_endowment("Coin")
for agent in self.world.agents
]))
elif self.planner_reward_type == "inv_income_weighted_utility":
curr_optimization_metric[
self.world.planner.idx] = rewards.inv_income_weighted_utility(
coin_endowments=np.array([
agent.total_endowment("Coin")
for agent in self.world.agents
]),
utilities=np.array([
curr_optimization_metric[agent.idx]
for agent in self.world.agents
]),
)
else:
print("No valid planner reward selected!")
raise NotImplementedError
return curr_optimization_metric
def get_current_optimization_metrics(self):
curr_optimization_metric = {}
# (for agents)
agent_reward = rewards.coin_eq_times_productivity(
coin_endowments=np.array([agent.total_endowment("Coin") for agent in self.world.agents]),
equality_weight=1 - self.mixing_weight_gini_vs_coin,
)
for agent in self.world.agents:
# scale reward to be close to original (utility rewards)
curr_optimization_metric[agent.idx] = agent_reward / 10.
# (for the planner)
if self.planner_reward_type == "coin_eq_times_productivity":
curr_optimization_metric[
self.world.planner.idx
] = rewards.coin_eq_times_productivity(
coin_endowments=np.array(
[agent.total_endowment("Coin") for agent in self.world.agents]
),
equality_weight=1 - self.mixing_weight_gini_vs_coin,
)
elif self.planner_reward_type == "inv_income_weighted_coin_endowments":
curr_optimization_metric[
self.world.planner.idx
] = rewards.inv_income_weighted_coin_endowments(
coin_endowments=np.array(
[agent.total_endowment("Coin") for agent in self.world.agents]
),
)
elif self.planner_reward_type == "inv_income_weighted_utility":
curr_optimization_metric[
self.world.planner.idx
] = rewards.inv_income_weighted_utility(
coin_endowments=np.array(
[agent.total_endowment("Coin") for agent in self.world.agents]
),
utilities=np.array(
[curr_optimization_metric[agent.idx] for agent in self.world.agents]
),
)
else:
print("No valid planner reward selected!")
raise NotImplementedError
return curr_optimization_metric
def scenario_metrics(self):
"""
Allows the scenario to generate metrics (collected along with component metrics
in the 'metrics' property).
To have the scenario add metrics, this function needs to return a dictionary of
{metric_key: value} where 'value' is a scalar (no nesting or lists!)
Here, summarize social metrics, endowments, utilities, and labor cost annealing.
"""
metrics = dict()
# Log social/economic indicators
coin_endowments = np.array(
[agent.total_endowment("Coin") for agent in self.world.agents])
pretax_incomes = np.array(
[agent.state["production"] for agent in self.world.agents])
metrics["social/productivity"] = social_metrics.get_productivity(
coin_endowments)
metrics["social/equality"] = social_metrics.get_equality(
coin_endowments)
utilities = np.array([
self.curr_optimization_metrics[agent.idx]
for agent in self.world.agents
])
metrics[
"social_welfare/coin_eq_times_productivity"] = rewards.coin_eq_times_productivity(
coin_endowments=coin_endowments, equality_weight=1.0)
metrics[
"social_welfare/inv_income_weighted_utility"] = rewards.inv_income_weighted_utility(
coin_endowments=pretax_incomes,
utilities=utilities # coin_endowments,
)
# Log average endowments, endogenous, and utility for agents
agent_endows = {}
agent_endogenous = {}
agent_utilities = []
for agent in self.world.agents:
for resource in agent.inventory.keys():
if resource not in agent_endows:
agent_endows[resource] = []
agent_endows[resource].append(agent.inventory[resource] +
agent.escrow[resource])
for endogenous, quantity in agent.endogenous.items():
if endogenous not in agent_endogenous:
agent_endogenous[endogenous] = []
agent_endogenous[endogenous].append(quantity)
agent_utilities.append(self.curr_optimization_metrics[agent.idx])
for resource, quantities in agent_endows.items():
metrics["endow/avg_agent/{}".format(resource)] = np.mean(
quantities)
for endogenous, quantities in agent_endogenous.items():
metrics["endogenous/avg_agent/{}".format(endogenous)] = np.mean(
quantities)
metrics["util/avg_agent"] = np.mean(agent_utilities)
# Log endowments and utility for the planner
for resource, quantity in self.world.planner.inventory.items():
metrics["endow/p/{}".format(resource)] = quantity
metrics["util/p"] = self.curr_optimization_metrics[
self.world.planner.idx]
return metrics