Implementing an Autonomous Reward Learning Agent with Google ADK
Explore the implementation of an autonomous reward learning agent inspired by the Eureka system, using Google ADK and Brax environments. Understand the agent orchestration, the iterative reward evolution loop, policy training, evaluation, and reflection processes. Gain a clear mental model of the system structure, execution flow, and how agentic design supports iterative reward engineering without needing deep coding knowledge.
In the previous lesson, we analyzed EUREKA as an autonomous reward learning agent. Now, we move from analysis to implementation.
Instead of reproducing NVIDIA’s full-scale robotic setup, we will implement a computationally lightweight Eureka-like system that preserves the core architectural principles in a more accessible and controlled setting. Our focus here is on understanding the system at a high level — how the agents are structured, how the reward evolution loop is orchestrated, and how the complete pipeline executes end-to-end.
We will not dive into every file or line of code. The goal is to build a clear mental model of how this implementation maps to the agentic architecture we previously studied. Learners who want a deeper, file-by-file breakdown can explore the full course version of this chapter.
For this hands-on demonstration, we will use:
Lightweight Brax environments (specifically HalfCheetah)
Google’s Agent Development Kit (ADK) for orchestration
Free T4 GPU resources on Google Colab
Throughout the lesson, we will examine:
The agents involved in the system
The role and responsibility of each agent
The inputs and outputs flowing through the workflow
The orchestration pattern managing the iterative reward evolution loop
The overall project structure
Finally, we will run the complete system once and inspect its outputs, trained policies, generated reward functions, and rollout visualizations, to observe the design in action.
Before we begin: A few key terms
Before we implement the system, we’ll define a few reinforcement learning terms that we’ll use throughout the chapter. You don’t need a deep RL background, just enough intuition to follow the mechanics.
Policy
A policy is the decision-making component of a reinforcement learning agent. Given the current state of the environment, the policy decides what action to take next. In our case, policy controls the HalfCheetah robot, deciding how each joint should move at every step. When we say “training a policy,” we mean optimizing this decision-making function so that the agent behaves better according to a reward signal.
Reward function
A reward function assigns a numerical score to the agent’s behavior at each step. For example:
Moving forward might give a positive reward.
Falling over might give a negative reward.
Wasting energy might incur a penalty.
Designing this function is difficult, and that is exactly the problem Eureka is trying to solve.