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70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Dict, List, Tuple
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import random
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@dataclass
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class ReinforcementAgent:
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"""
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A simple reinforcement learning agent using tabular Q-learning.
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Attributes
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----------
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q_values : Dict[Tuple[str, str], float]
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Q-value table mapping (state, action) pairs to their value estimates.
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"""
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q_values: Dict[Tuple[str, str], float] = field(default_factory=dict)
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def update(self, state: str, action: str, reward: float, alpha: float = 0.1) -> None:
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"""
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Update the Q-value for a state-action pair.
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Parameters
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----------
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state : str
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Current state identifier.
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action : str
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Action taken in the state.
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reward : float
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Reward received for this state-action.
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alpha : float, default 0.1
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Learning rate.
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"""
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key = (state, action)
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old = self.q_values.get(key, 0.0)
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self.q_values[key] = old + alpha * (reward - old)
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def choose_action(self, state: str, actions: List[str], epsilon: float = 0.2) -> str:
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"""
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Choose an action using an epsilon-greedy policy.
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Parameters
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----------
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state : str
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Current state identifier.
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actions : List[str]
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Available actions.
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epsilon : float
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Exploration rate.
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Returns
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-------
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str
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Selected action.
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"""
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if not actions:
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raise ValueError("actions list cannot be empty")
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if random.random() < epsilon:
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return random.choice(actions)
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# choose action with highest Q-value
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return max(actions, key=lambda a: self.q_values.get((state, a), 0.0))
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if __name__ == "__main__":
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agent = ReinforcementAgent()
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state = "home"
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actions = ["explore", "rest"]
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chosen = agent.choose_action(state, actions)
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agent.update(state, chosen, reward=1.0)
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print(agent.q_values)
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