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Added IDP agent
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6 files changed

+410
-2
lines changed

‎ddopai/_modidx.py‎

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'ddopai/agents/dynamic_pricing/greedy.py'),
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'ddopai.agents.dynamic_pricing.greedy.GreedyPolicy.update_task': ( '30_agents/42_DP_agents/greedy_agent.html#greedypolicy.update_task',
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'ddopai/agents/dynamic_pricing/greedy.py')},
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP': { 'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPAgent': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpagent',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPAgent.__init__': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpagent.__init__',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPAgent.update_task': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpagent.update_task',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPCoreAgent': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpcoreagent',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPCoreAgent.__init__': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpcoreagent.__init__',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPCoreAgent.fit': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpcoreagent.fit',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPCoreAgent.update_task': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idpcoreagent.update_task',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy.__init__': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy.__init__',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy.draw_action': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy.draw_action',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy.fit': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy.fit',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy.lagrangian': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy.lagrangian',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy.reset': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy.reset',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py'),
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'ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPPolicy.update_task': ( '30_agents/42_DP_agents/421_DP_inventory_agents/idp_agent.html#idppolicy.update_task',
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'ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py')},
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'ddopai.agents.dynamic_pricing.mushroom_rl': { 'ddopai.agents.dynamic_pricing.mushroom_rl.PricingMushroomBaseAgent': ( '30_agents/42_DP_agents/mushroom_base_agent.html#pricingmushroombaseagent',
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'ddopai/agents/dynamic_pricing/mushroom_rl.py'),
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'ddopai.agents.dynamic_pricing.mushroom_rl.PricingMushroomBaseAgent.__init__': ( '30_agents/42_DP_agents/mushroom_base_agent.html#pricingmushroombaseagent.__init__',

‎ddopai/agents/class_names.py‎

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"TS": "ddopai.agents.dynamic_pricing.TS.TSAgent",
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"UCB": "ddopai.agents.dynamic_pricing.UCB.UCBAgent",
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"Clairvoyant": "ddopai.agents.dynamic_pricing.clairvoyant.ClairvoyantAgent",
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"MTS": "ddopai.agents.dynamic_pricing.MTS.MTSAgent"
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"MTS": "ddopai.agents.dynamic_pricing.MTS.MTSAgent",
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"IDP": "ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPAgent"
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}

‎ddopai/agents/dynamic_pricing/inventory_constrained/IDP.py‎

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"""Agents that knows the underlying task and the optimal action"""
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../../../nbs/30_agents/42_DP_agents/421_DP_inventory_agents/10_IDP_agent.ipynb.
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# %% auto 0
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__all__ = ['IDPPolicy', 'IDPCoreAgent', 'IDPAgent']
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# %% ../../../../nbs/30_agents/42_DP_agents/421_DP_inventory_agents/10_IDP_agent.ipynb 3
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import logging
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from abc import ABC, abstractmethod
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from typing import Union, Optional, List
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import numpy as np
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import joblib
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import os
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import statsmodels.api as sm
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from ..utils import GLMLink
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from ....envs.base import BaseEnvironment
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from ..mushroom_rl import PricingMushroomBaseAgent
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from mushroom_rl.core import Agent
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from ....utils import MDPInfo
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from ...obsprocessors import FlattenTimeDimNumpy
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from ....envs.actionprocessors import ClipAction
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# %% ../../../../nbs/30_agents/42_DP_agents/421_DP_inventory_agents/10_IDP_agent.ipynb 4
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class IDPPolicy():
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def __init__(self,
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environment_info: MDPInfo,
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obsprocessors: Optional[List[object]] = None,
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actionprocessors: Optional[List[object]] = None,
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agent_name: str | None = None,
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task: dict = None,
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price_function = None,
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g = None,
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):
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alpha = np.array(task["alpha"])
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beta = np.array(task["beta"])
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assert type(alpha) == type(beta), "alpha and beta must be of the same type"
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if type(alpha) == None:
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alpha = np.zeros(environment_info.observation_space['features'].shape[0])
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beta = np.zeros(environment_info.observation_space['features'].shape[0])
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self.environment_info = environment_info
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self.task = task
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self.T = task["horizon"]
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self.alpha = alpha
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self.beta = beta
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if environment_info.observation_space['features'].shape[0] == 1:
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self.E_X = np.array([1])
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else:
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self.E_X = np.full(environment_info.observation_space['features'].shape[0], 1 / (2 * np.sqrt(environment_info.observation_space['features'].shape[0])))
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self.actionprocessors = actionprocessors
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self.price_function = price_function # Needs to return an np array
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self.g = g
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self.t = 0
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self.mode = "train"
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self.actionprocessors.append(ClipAction(environment_info.action_space.low, environment_info.action_space.high))
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def draw_action(self, observation: np.ndarray):
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X = observation['features']
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B_t = observation['Inventory']
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price = self.price_function(X, self.alpha, self.beta)
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lagrangian = self.lagrangian(B_t)
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price = price + lagrangian
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for processor in self.actionprocessors:
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price = processor(price)
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return np.array(price)
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def lagrangian(self, B_t):
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"""
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Lagrangian function for the pricing problem
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"""
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avg_remaining_B = (2 * B_t) / (self.T - self.t +1)
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lagrangian = (avg_remaining_B - np.dot(self.alpha, self.E_X)) / np.dot(self.beta, self.E_X)
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return lagrangian
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def update_task(self, env):
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self.environment_info = env.mdp_info
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self.task = env.get_task()
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self.alpha = np.array(self.task["alpha"])
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self.beta = np.array(self.task["beta"])
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if self.environment_info.observation_space['features'].shape[0] == 1:
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self.E_X = np.array([1])
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else:
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self.E_X = np.full(self.environment_info.observation_space['features'].shape[0], 1 / (2 * np.sqrt(self.environment_info.observation_space['features'].shape[0])))
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self.T = self.task["horizon"]
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"""TODO add change in price function"""
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def fit(self, X, Y, action):
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self.t += 1
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def reset(self):
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pass
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# %% ../../../../nbs/30_agents/42_DP_agents/421_DP_inventory_agents/10_IDP_agent.ipynb 5
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class IDPCoreAgent(Agent):
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"""
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Base class for clairvoyant agents.
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"""
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def __init__(self,
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environment_info: MDPInfo,
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obsprocessors: Optional[List[object]] = [],
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actionprocessors: Optional[List[object]] = [],
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agent_name: str | None = None,
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task: dict = None,
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price_function = None,
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g = None,
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):
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policy = IDPPolicy(environment_info=environment_info, obsprocessors=obsprocessors, actionprocessors=actionprocessors, task=task, price_function=price_function, g=g)
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self.agent_name = agent_name
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super().__init__(environment_info, policy)
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def fit(self, dataset, **kwargs):
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X = dataset[0][0]["features"]
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Y = kwargs["demand"][0]
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action = dataset[0][1]
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self.policy.fit(X, Y, action)
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def update_task(self, env):
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self.policy.update_task(env)
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# %% ../../../../nbs/30_agents/42_DP_agents/421_DP_inventory_agents/10_IDP_agent.ipynb 6
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class IDPAgent(PricingMushroomBaseAgent):
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"""
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Wrapper class for IDPCoreAgent to interact with MushroomRL.
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"""
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def __init__(self,
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environment_info: MDPInfo,
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obsprocessors: Optional[List[object]] =[],
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actionprocessors: Optional[List[object]] = [],
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agent_name: str | None = None,
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task: dict = None,
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price_function = None,
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g = None,
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):
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self.agent = IDPCoreAgent(environment_info = environment_info,
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obsprocessors = obsprocessors,
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actionprocessors = actionprocessors,
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agent_name = agent_name,
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task=task,
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price_function = price_function,
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g = g)
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super().__init__(environment_info = environment_info, obsprocessors = obsprocessors, agent_name = agent_name)
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def update_task(self, env: object):
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""" Update the environment specific parameters of the agent """
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self.agent.update_task(env)
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‎ddopai/agents/dynamic_pricing/inventory_constrained/__init__.py‎

Whitespace-only changes.

‎nbs/30_agents/40_base_agents/10_AGENT_CLASSES.ipynb‎

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" \"TS\": \"ddopai.agents.dynamic_pricing.TS.TSAgent\",\n",
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" \"UCB\": \"ddopai.agents.dynamic_pricing.UCB.UCBAgent\",\n",
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" \"Clairvoyant\": \"ddopai.agents.dynamic_pricing.clairvoyant.ClairvoyantAgent\",\n",
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" \"MTS\": \"ddopai.agents.dynamic_pricing.MTS.MTSAgent\"\n",
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" \"MTS\": \"ddopai.agents.dynamic_pricing.MTS.MTSAgent\",\n",
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" \n",
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" \"IDP\": \"ddopai.agents.dynamic_pricing.inventory_constrained.IDP.IDPAgent\"\n",
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"}"
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]
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},

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