class BaseLendingEnv(core.FairnessEnv):
"""Base loan decision environment.
In each step, the agent decides whether to accept or reject an
application.
The base class is abstract.
"""
metadata = {'render.modes': ['human']}
default_param_builder = lending_params.Params
group_membership_var = 'group'
_cash_updater = _CashUpdater()
_parameter_updater = core.NoUpdate()
_applicant_updater = _ApplicantSampler()
def __init__(self, params=None):
params = (self.default_param_builder() if params is None else params
) # type: lending_params.Params
# The action space of the agent is Accept/Reject.
self.action_space = spaces.Discrete(2)
# Bank's cash is a scalar and cannot be negative.
bank_cash_space = spaces.Box(low=0,
high=params.max_cash,
shape=(),
dtype=np.float32)
# Two-dimensional observation space describes each loan applicant.
loan_applicant_space = spaces.Box(
params.min_observation,
params.max_observation,
dtype=np.float32,
shape=(params.applicant_distribution.dim, ))
group_space = spaces.MultiBinary(params.num_groups)
self.observable_state_vars = {
'bank_cash': bank_cash_space,
'applicant_features': loan_applicant_space,
'group': group_space
}
super(BaseLendingEnv, self).__init__(params)
self._state_init()
def _state_init(self, rng=None):
self.state = State(
# Copy in case state.params get mutated, initial_params stays pristine.
params=copy.deepcopy(self.initial_params),
rng=rng or np.random.RandomState(),
bank_cash=self.initial_params.bank_starting_cash)
self._applicant_updater.update(self.state, None)
def reset(self):
"""Resets the environment."""
self._state_init(self.state.rng)
return super(BaseLendingEnv, self).reset()
def _is_done(self):
"""Returns True if the bank cash is less than loan_amount."""
return self.state.bank_cash < self.state.params.loan_amount
def _step_impl(self, state, action):
"""Run one timestep of the environment's dynamics.
In a single step, the agent decides whether to accept or reject an
application.
The potential payoffs of rejected application are always 0.
If an application is accepted, the payoffs are:
-loan_amount if the applicant defaults.
+loan_amount*interest_rate if the applicant successfully pays back.
Args:
state: A `State` object containing the current state.
action: An action in `action_space`.
Returns:
A `State` object containing the updated state.
"""
self._cash_updater.update(self.state, action)
self._parameter_updater.update(self.state, action)
self._applicant_updater.update(self.state, action)
return self.state
def render(self, mode='human'):
"""Renders the history and current state using matplotlib.
Args:
mode: string indicating the rendering mode. The only supported mode is
`human`.
"""
if mode == 'human':
if self.state.params.applicant_distribution.dim != 2:
raise NotImplementedError(
'Cannot render if applicant features are not exactly 2 dimensional. '
'Got %d dimensional applicant features.' %
self.state.params.applicant_distribution.dim)
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.title('Applicant Features')
plt.xticks([], [])
plt.yticks([], [])
for state, action in self.history:
if action == 1:
x, y = state.applicant_features
color = 'r' if state.will_default else 'b'
plt.plot([x], [y],
_MARKERS[state.group_id] + color,
markersize=12)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
x, y = self.state.applicant_features
plt.plot([x], [y],
_MARKERS[self.state.group_id] + 'k',
markersize=15)
plt.subplot(1, 2, 2)
plt.title('Cash')
plt.plot([state.bank_cash
for state, _ in self.history] + [self.state.bank_cash])
plt.ylabel('# loans available')
plt.xlabel('Time')
plt.tight_layout()
else:
super(BaseLendingEnv,
self).render(mode) # Raises NotImplementedError
def test_noop_state_updater_does_nothing(self):
env = test_util.DummyEnv()
state = env._get_state()
before = copy.deepcopy(state)
core.NoUpdate().update(state, env.action_space.sample())
self.assertEqual(state, before)