cart pole - chunhualiao/public-docs GitHub Wiki

reinforcement learning

https://gist.github.com/chunhualiao/dad878d8ff55f19c8cde013568f37d82

  1. CartPole Environment (Intermediate)
  • Project: Solve the CartPole environment from Gymnasium. The goal is to balance a pole on a cart by moving the cart left or right.
  • Concepts:
    • Introduces continuous state space (pole angle and velocity, cart position and velocity) but discrete action space (left or right).
    • Often solved with Deep Q-Networks (DQN) or policy gradient methods, but simpler versions can be solved with discretized states and tabular methods or basic neural networks for function approximation.
  • Complexity: Moderate. Introduces continuous states and the need for function approximation for more efficient learning in continuous spaces.
  • Tools: gymnasium, TensorFlow or PyTorch for neural networks (if using function approximation).
  • Next Step: Implement a basic neural network to approximate the Q-function instead of using a Q-table.

Summary of Thinking Process

The thought process outlines a structured approach to implementing a Deep Q-Network (DQN) to solve the CartPole-v1 problem using reinforcement learning, optimized for execution on a MacBook Air M3. The reasoning follows these main steps:

  1. Understanding the Problem

    • The CartPole-v1 environment requires balancing a pole on a cart by moving left or right.
    • The state space consists of four continuous variables (cart position, velocity, pole angle, and angular velocity), and the action space is discrete (left or right).
    • The reward structure is simple: +1 per time step the pole remains upright.
    • The episode ends if the pole falls, the cart moves out of bounds, or after 500 steps.

  2. Choosing the Reinforcement Learning Algorithm

    • DQN (Deep Q-Network) is selected since it's well-suited for environments with low-dimensional continuous state spaces.
    • Components required for DQN:
      • A neural network to approximate Q-values.
      • Experience replay buffer to store and sample transitions.
      • A target network to stabilize learning.
      • An ε-greedy policy for exploration-exploitation balance.

  3. Designing the Neural Network

    • Input: 4-dimensional state
    • Output: 2 Q-values (one for each action)
    • Architecture:
      • Two hidden layers (64, 64 units) with ReLU activations
      • Optimizer: Adam with a learning rate of 0.001
    • Consideration for MPS acceleration (for MacBooks) via PyTorch.

  4. Setting Up DQN Components

    • Experience Replay Buffer (stores transitions for better learning stability).
    • Target Network (updated every 100 steps for stability).
    • Epsilon-Greedy Exploration:
      • Starts at ε = 1.0, decays over time to ε_min = 0.01.
      • Decay should happen once per episode, not per step (to ensure gradual exploration).

  5. Implementation Strategy

    • Define a DQN model class (subclass of torch.nn.Module).
    • Implement an Agent class handling:
      • Experience replay
      • Action selection (ε-greedy policy)
      • Training using the Bellman equation
      • Updating the target network
    • Implement Training Loop:
      • Reset the environment for each episode
      • Execute actions and collect experiences
      • Train the network and periodically update the target network
      • Decay epsilon at the end of each episode

  6. Hyperparameter Tuning & Debugging

    • Addressed common pitfalls:
      • Epsilon decay placement (ensuring gradual rather than abrupt decay).
      • Handling of done and truncated flags (ensuring correct termination detection).
      • Target network update frequency (every 100 steps).
      • Correcting tensor operations (e.g., ensuring proper dimensions for Q-value computations).
    • Considered alternative architectures and optimizations, balancing efficiency for MacBook Air.

Key Refinements

  • Target network updates every 100 steps instead of every episode.
  • Epsilon decay applied per episode, preventing premature exploitation.
  • Experience replay buffer size adjusted for efficiency on a MacBook.
  • Bug fixes (correcting tensor operations, properly handling terminal states).

Final Thoughts

This structured approach ensures that the DQN implementation is efficient, correct, and optimized for MacBook Air's hardware. The thought process balances algorithmic rigor, computational efficiency, and practical implementation details, leading to a well-structured reinforcement learning agent for CartPole-v1. 🚀