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Robbie edited this page Apr 27, 2026 · 1 revision

G.O.D Framework

Script:ai_reinforcement_learning.py

An advanced module for implementing reinforcement learning agents with reward-based adaptation.

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Introduction

Theai_reinforcement_learning.pymodule is designed to create and manage reinforcement learning (RL) agents. The goal is to build systems that learn optimal policies for decision-making tasks by interacting with an environment and optimizing for long-term rewards. This module is critical for adaptive AI applications like robotics, game simulation, and real-time strategy optimization.

Purpose

The purpose of this script is to:

  • Enable reinforcement learning within the G.O.D Framework.
  • Provide tools to define environments, actions, rewards, and agents.
  • Allow training of RL agents using both value-based and policy-based approaches.
  • Support custom reward functions and dynamic simulation environments.
  • Integrate with other modules to adapt to real-world environments in real-time.

Key Features

  • **Flexible Environment Integration:**Supports dynamic environments for agent training.
  • **Reward-based Optimization:**Allows definition of custom reward mechanisms to align with domain goals.
  • **Modular Agent Design:**Includes both pre-configured agents and tools to build custom RL agents.
  • **Algorithm Support:**Provides implementations for popular algorithms, including Q-Learning, Deep Q-Networks (DQN), and Actor-Critic.
  • **Visualization Tools (Optional):**Offers real-time logging and visualization of reward progress.

Logic and Implementation

The script provides basic reinforcement learning functionality, including the environment-action interaction loop, reward mechanisms, and learning policies. Below is an example implementation: import numpy as np class Environment: """ A simple environment simulator for reinforcement learning. """ def __init__(self, goal_state): self.state = 0 # Starting state self.goal_state = goal_state # Desired goal state def reset(self): self.state = 0 return self.state def step(self, action): """ Takes an action and updates the environment state. Args: action (int): The action taken by the agent (0 for +1, 1 for -1). Returns: tuple: New state, reward, done (whether the episode is complete), info """ if action == 0: self.state += 1 elif action == 1: self.state -= 1 reward = 1 if self.state == self.goal_state else -0.1 done = self.state == self.goal_state return self.state, reward, done, {} class QLearningAgent: """ A reinforcement learning agent implementing Q-Learning. """ def __init__(self, state_space, action_space, learning_rate=0.1, discount_factor=0.95, exploration_rate=1.0): self.state_space = state_space self.action_space = action_space self.alpha = learning_rate self.gamma = discount_factor self.epsilon = exploration_rate self.q_table = np.zeros((state_space, action_space)) # Initialize Q-table def choose_action(self, state): """ Chooses an action using the epsilon-greedy policy. Args: state (int): Current state. Returns: int: Action to be taken. """ if np.random.random()<self.epsilon: return np.random.choice(self.action_space) return np.argmax(self.q_table[state]) def learn(self, state, action, reward, next_state): """ Updates the Q-table using the Bellman equation. Args: state (int): Current state. action (int): Action taken. reward (float): Reward received. next_state (int): Next state. """ best_next_action = np.argmax(self.q_table[next_state]) td_target = reward + self.gamma * self.q_table[next_state, best_next_action] self.q_table[state, action] += self.alpha * (td_target - self.q_table[state, action]) # Example Usage if __name__ == "__main__": # Create the environment and agent env = Environment(goal_state=5) agent = QLearningAgent(state_space=10, action_space=2) for episode in range(100): state = env.reset() done = False while not done: action = agent.choose_action(state) next_state, reward, done, _ = env.step(action) agent.learn(state, action, reward, next_state) state = next_state print("Training complete!")

Dependencies

  • numpy: A mathematical library for matrix operations used in Q-Learning.

Integration with the G.O.D Framework

Theai_reinforcement_learning.pymodule integrates with key G.O.D Framework components:

  • **ai_environment_manager.py:**Manages dynamic environments for RL agent interactions.
  • **ai_feedback_collector.py:**Provides feedback for rewards by monitoring agent behavior.
  • **ai_error_tracker.py:**Logs actions or strategies that underperform, ensuring improvements in future episodes.

Future Enhancements

Planned improvements for this module include:

  • Integration of deep reinforcement learning methods, such as DDPG and PPO.
  • Parallelized training across multiple environments using frameworks like Ray or Stable-Baselines.
  • Integration of complex reward functions for real-world simulation tasks.
  • Support for visualization tools such as TensorBoard for analyzing agent performance.

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