75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
import numpy as np
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from Environment import Env
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np.random.seed(1)
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class DynaQ:
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def __init__(self, env: Env, episodes: int, epsilon: float, alpha: float, gamma: float, n_steps: int):
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# Initialize parameter
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self.env = env
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self.alpha = alpha
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self.gamma = gamma
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self.epsilon = epsilon
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self.episodes = episodes
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self.n_steps = n_steps
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self.time_step = 0
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self.state = self.env.start
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self.steps_per_episode = []
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self.state_actions = []
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self.step_in_episode = 0
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# Initialize Q-matrix and model
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self.Q = {}
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self.model = {}
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for state in list(self.env.G):
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self.Q[state] = {}
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self.model[state] = {}
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for action in list(self.env.G.neighbors(state)) + [state]:
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self.Q[state][action] = 0
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self.model[state][action] = (-1, action, 0)
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'''
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Resets the model
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'''
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def reset(self) -> None:
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self.state = self.env.start
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self.state_actions = []
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self.step_in_episode = 0
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'''
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Learning method for agent
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Basically DynaQ algorithm adapted for graphs
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'''
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def learn(self, epsilon_decay: float, epsilon_min: float, run: int) -> None:
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# todo: implement learning
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pass
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'''
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Returns epsilon-greedy action
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'''
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def get_action(self, eps: float) -> int:
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# todo: implement eval
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pass
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'''
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Add Reward, next state and current time step to state-action pair in model
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'''
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def update_model(self, state: int, action: int, reward: float, next_state) -> None:
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self.model[state][action] = (reward, next_state, self.time_step)
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'''
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Planning phase, basically Bellmann equation with already taken state-action pairs
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'''
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def planning(self) -> None:
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# todo: implement planning
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pass
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