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Reinforcement Learning Algorithms in AGI-REPOSITORY

This document provides comprehensive information on the reinforcement learning algorithms used in the AGI-REPOSITORY project. It includes examples and explanations of Q-learning and policy gradient methods, and references the src/learning/reinforcement_learning.py file for implementation details.

Q-Learning

Q-learning is a model-free reinforcement learning algorithm that aims to learn the value of an action in a particular state. The agent uses a Q-table to store the value of each state-action pair and updates the table based on the rewards received from the environment.

Implementation

The Q-learning algorithm is implemented in the QLearningAgent class in the src/learning/reinforcement_learning.py file. Below is an example of how to use the QLearningAgent class:

from src.learning.reinforcement_learning import QLearningAgent

# Define the state and action sizes
state_size = 10
action_size = 4

# Create a Q-learning agent
agent = QLearningAgent(state_size, action_size)

# Example of choosing an action and learning from a transition
state = 0
action = agent.choose_action(state)
reward = 1
next_state = 1
agent.learn(state, action, reward, next_state)

Explanation

  1. Initialization: The QLearningAgent class is initialized with the state size, action size, learning rate, discount factor, exploration rate, and exploration decay.
  2. Choosing an Action: The choose_action method selects an action based on the exploration rate. If a random number is less than the exploration rate, a random action is chosen; otherwise, the action with the highest Q-value is selected.
  3. Learning from a Transition: The learn method updates the Q-table based on the reward received and the next state. The Q-value for the current state-action pair is updated using the temporal difference (TD) error.

Policy Gradient

Policy gradient methods aim to optimize the policy directly by adjusting the parameters of the policy based on the rewards received. The agent learns a probability distribution over actions for each state and updates the policy to maximize the expected reward.

Implementation

The policy gradient algorithm is implemented in the PolicyGradientAgent class in the src/learning/reinforcement_learning.py file. Below is an example of how to use the PolicyGradientAgent class:

from src.learning.reinforcement_learning import PolicyGradientAgent

# Define the state and action sizes
state_size = 10
action_size = 4

# Create a policy gradient agent
agent = PolicyGradientAgent(state_size, action_size)

# Example of choosing an action and storing a transition
state = 0
action = agent.choose_action(state)
reward = 1
agent.store_transition(state, action, reward)

# Learn from the stored transitions
agent.learn()

Explanation

  1. Initialization: The PolicyGradientAgent class is initialized with the state size, action size, and learning rate.
  2. Choosing an Action: The choose_action method selects an action based on the probability distribution over actions for the given state.
  3. Storing a Transition: The store_transition method stores the state, action, and reward for each transition.
  4. Learning from Transitions: The learn method updates the policy based on the stored transitions. The policy is adjusted to increase the probability of actions that led to higher rewards.

Continuous Integration for Reinforcement Learning

Continuous integration (CI) is a crucial process in software development that ensures code changes are automatically tested and validated. In the context of reinforcement learning, CI helps maintain the reliability and performance of the learning algorithms by running automated tests on the agents.

Implementation

The continuous integration pipeline for reinforcement learning is implemented in the run_continuous_integration function in the src/learning/reinforcement_learning.py file. This function runs unit tests for the QLearningAgent and PolicyGradientAgent classes to ensure they function correctly.

Benefits

  1. Automated Testing: CI allows for automated testing of reinforcement learning agents, reducing the need for manual testing and ensuring that code changes do not introduce errors.
  2. Early Detection of Issues: By running tests automatically, CI helps detect issues early in the development process, allowing for quicker resolution and maintaining code quality.
  3. Consistent Performance: CI ensures that reinforcement learning algorithms perform consistently by validating their behavior with each code change.

References

For more details on the implementation of these algorithms and the continuous integration process, refer to the src/learning/reinforcement_learning.py file in the AGI-REPOSITORY project.