Introduction
The ai_reinforcement_learning.py module 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
The ai_reinforcement_learning.py module 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.