Implementing an Autonomous Reward Learning Agent with Google ADK
Explore the process of implementing an autonomous reward learning agent based on a Eureka-like architecture using Google ADK and Brax. Understand key concepts like reward functions, policy training, rollout visualization, and agent orchestration in a multi-step iterative reward evolution loop. Gain insight into the system's project structure and execution flow to build a clear mental model of reinforcement learning agent design in practical environments.
In the previous lesson, we analyzed EUREKA as an autonomous reward learning agent. Now, we move from analysis to implementation.
This implementation uses a simplified Eureka-like system that preserves core architectural principles in a controlled, computationally efficient setting. The focus is on high-level system understanding, including agent structure, reward evolution loop orchestration, and end-to-end pipeline execution.
This section does not cover every file or line of code. The goal is to develop a clear understanding of how this implementation maps to the previously introduced agent-based architecture. For a detailed, file-level breakdown, refer to 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.
Rollout
A rollout is a recorded execution of a policy in the environment. Think of it as:
“Let the trained agent run for several steps and watch what it actually does.”
Rollouts are visualized as ...