Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent
This guide reimagines a classic deep Q-learning workflow by assembling a lean stack built around the JAX ecosystem. The aim is to reveal how an agent learns to balance a pole by interacting with the CartPole environment. Rather than relying on a full RL framework, the pieces are connected directly to show the data flow, decision making, and parameter updates. The learning signals align with RLax-inspired primitives for Q-learning, while modeling uses Haiku, optimization with Optax, and fast numerical execution via JAX.
The setup uses a compact neural network to estimate Q-values, a replay memory to break temporal correlations, and a learning rule based on temporal-difference errors. This arrangement highlights the modular nature of modern RL tooling and makes it easier to swap components during experimentation.
Core components
Environment and network: The agent tackles a classic control task where the objective is to keep a pole upright on a moving cart. A small multi-layer perceptron serves as the Q-network, taking the current state as input and producing a value for each possible action. An identical architecture is initialized as a target network to stabilize learning by providing a fixed reference during updates.
Experience storage: A finite replay buffer holds recent transitions (state, action, reward, next state, done). Sampling mini-batches from this pool helps decorrelate samples and yields more reliable gradient estimates, while operations to append new experiences and retrieve batches are kept lightweight.
Action exploration: An epsilon-greedy policy balances exploitation and exploration. Most steps select the action with the highest predicted Q-value, with occasional random choices to probe new situations. Epsilon decreases over time to emphasize exploitation as the agent improves.
Learning signal
Updates follow a Bellman-style target: the current Q-value for a state-action pair is updated toward a target composed of the immediate reward plus the discounted maximum Q-value of the next state, as evaluated by the target network. A robust loss function, such as Huber loss, helps dampen the influence of outliers. Gradients are computed automatically, and the online network’s parameters are adjusted via a gradient-based optimizer.
Training loop
Learning unfolds across episodes. Each step involves selecting an action, applying it to the environment, observing the outcome, and storing the transition. When enough experiences exist, a batch is drawn to compute gradients and perform an update. Periodically, the target network is refreshed to reflect the online network’s latest parameters. Throughout training, metrics like episodic returns and loss values are tracked to monitor progress.
Evaluation and tuning
Evaluation runs occur without exploration noise to gauge true policy performance. Key hyperparameters include the learning rate, discount factor, batch size, replay capacity, and the cadence of target-network updates. A well-designed epsilon schedule and a suitably sized replay buffer are crucial for stable convergence and longer balancing episodes.
Takeaways and extensibility
The approach demonstrates how a lean, modular stack can realize a robust Q-learning agent from first principles. By leveraging reusable components for modeling, optimization, and learning signals, researchers can rapidly experiment with variations—such as double Q-learning, distributional methods, or actor-critic hybrids—without committing to a single monolithic framework. The structure adapts to different environments and action spaces, with appropriate adjustments to the Q-function design and loss formulation.
