End-to-End Test: Implementing Observation-Decision Loop

Nov 28, 2025 by Alex Johnson 56 views

Implementing the Observation-Decision Loop Test: A Key Step in End-to-End Pipeline Validation

In the realm of game development and AI-driven systems, ensuring a seamless interaction between perception, decision-making, and action is paramount. This article delves into the critical process of implementing an observation-decision loop test, a vital component in validating the end-to-end pipeline of an intelligent agent. This test focuses on verifying that game observations can be accurately transmitted to a Python backend, processed into informed decisions, and then effectively relayed back to the Godot game engine – all without the agent physically executing any movements. This foundational step is crucial for building robust and responsive AI agents. Let's explore the intricacies of this process and why it is indispensable for a successful AI integration.

Current Testing Status and the Missing Link

Before diving into the specifics of the observation-decision loop test, it's essential to understand the current testing landscape. We have already successfully validated several key components of our system. Backend connectivity has been confirmed through test_autoload_services.gd, ensuring that our game engine can communicate with the backend server. Tool execution, a core element of agent action, has been tested using test_tool_execution.gd, verifying that the agent can utilize various tools within the game environment. Furthermore, HTTP communication has been validated via test_tool_execution_simple.tscn, guaranteeing the reliable exchange of data between the game and the backend.

However, a critical piece of the puzzle remains: the observation-based decision loop. This loop represents the agent's ability to perceive its environment, make decisions based on that perception, and initiate actions accordingly. Currently, we are missing tests that specifically address the Perception → Decision → Action cycle, as well as a continuous tick loop that simulates the agent's ongoing interaction with the backend. The observation-decision loop test aims to bridge this gap, providing a comprehensive assessment of this vital functionality. Without this, we cannot be certain that our agent can react appropriately to the dynamic environment it inhabits. This test will set the stage for more complex behaviors and interactions in the future.

Architecture Flow: A Step-by-Step Breakdown

To fully grasp the significance of the observation-decision loop test, let's dissect its architectural flow. The process begins with a simplified test scene within the Godot game engine. This scene includes mock elements such as the agent's position and simulated resources and hazards. This controlled environment allows us to isolate and evaluate the decision-making process without the complexities of a full game simulation. The first step is to build observations from this mock environment, creating a structured representation of the agent's surroundings.

These observations are then packaged into an Observation Dictionary, which contains key information such as the agent's position (x, y, z coordinates), nearby resources, and nearby hazards. This dictionary serves as the input for the agent's decision-making process. The dictionary is structured so that all the relevant information for the AI's needs is passed as a single object. This format makes it easier to manage and update the information over time.

The Observation Dictionary is then transmitted to the Python Backend via an HTTP POST request to the /observe endpoint. This backend houses the agent's decision logic, which, for this test, includes a mock decision-making function. The function processes the observations and returns a decision, specifying the tool to use, the associated parameters, and the reasoning behind the choice. This is crucial for debugging and understanding why the agent is behaving in a particular way. The backend's ability to return structured decisions is vital for ensuring that the agent can execute the chosen action effectively.

The backend's decision is then sent back to the Test Scene as a JSON response. Within the scene, the decision is logged for analysis, but the action itself is not executed. This allows us to focus solely on the decision-making process and ensure its accuracy. The loop then continues, simulating the continuous interaction between the agent and its environment. This iterative process of observation, decision, and logging provides a detailed record of the agent's behavior under various simulated conditions. The focus on the logging rather than execution at this phase makes it easier to identify and rectify logical errors in the decision making process.

Implementation Tasks: A Phased Approach

The implementation of the observation-decision loop test is structured into three distinct phases, each with specific goals and tasks. This phased approach ensures a systematic and efficient development process.

Phase 1: Backend Endpoint (Estimated Time: 30 minutes)

The initial phase focuses on the backend component, specifically the python/ipc/server.py file. The primary task is to add an /observe POST endpoint, which will serve as the entry point for observation data from the game engine. This endpoint needs to be robust and capable of handling incoming data efficiently. Alongside this, we need to implement a make_mock_decision() function. This function will embody the agent's decision logic, using a rule-based approach for this initial test. The logic will prioritize actions based on the following criteria: first, avoiding nearby hazards (within a distance of 3.0 units); second, moving to the nearest resource (within a distance of 5.0 units); and finally, defaulting to an idle state if no immediate actions are required. The function needs to return a decision dictionary containing the tool name, parameters, and a clear reasoning for the decision. To facilitate debugging, logging is added to record the observations received and the decisions made by the backend. This detailed logging provides invaluable insights into the decision-making process and allows for quick identification of any logical errors. The modularity of this function ensures that it can be easily replaced with more sophisticated decision-making processes, including those powered by machine learning, in the future.

Phase 2: Test Scene (Estimated Time: 1 hour)

The second phase shifts the focus to the game engine side, specifically the creation of a test scene within Godot. This involves creating a new script, scripts/tests/test_observation_loop.gd, which extends the Node class. This script will house the logic for the test. The first step is to add mock foraging data, including the agent's position and the positions of resources and hazards within the simulated environment. This mock data will drive the agent's decision-making during the test. Next, we need to implement the build_observation() function, which will construct the Observation Dictionary from the mock data. This function will take the agent's position and the surrounding environment into account, packaging the relevant information into a structured dictionary format. We also need to implement the send_observation() function, which will use the HTTPRequest node to transmit the Observation Dictionary to the backend's /observe endpoint. This function will handle the communication between the game engine and the backend server. The test will then process 10 ticks, with a 0.5-second delay between each tick. This simulates the continuous interaction between the agent and its environment. During each tick, the agent will send an observation to the backend, receive a decision, and log the results. The script will also log the observations sent and the decisions received, providing a detailed record of the agent's behavior over time. Finally, keyboard controls are added, allowing the tester to press 'Q' to quit the test. This provides a convenient way to terminate the test manually. The corresponding scene, scenes/tests/test_observation_loop.tscn, will be a simple scene containing the test script node. A 3D environment is not required for this test, simplifying the setup and focusing the test on the core logic of the observation-decision loop.

Phase 3: Documentation (Estimated Time: 30 minutes)

The final phase emphasizes the importance of clear and comprehensive documentation. A new section is added to the scenes/tests/README.md file specifically for test_observation_loop.tscn. This documentation will describe the purpose of the test, explaining what it aims to achieve and why it is important. It will also detail how to run the test, providing step-by-step instructions for executing the test scene within Godot. Furthermore, it will outline the expected output, describing what the console output should look like and how to interpret the results. Finally, the test is added to the test suite list, ensuring that it is included in future testing efforts. This documentation serves as a valuable resource for developers and testers, ensuring that the test is understood and can be used effectively. Good documentation is vital for maintaining and improving the system over time.

Mock Decision Logic: A Rule-Based Approach

The make_mock_decision() function is the heart of the agent's decision-making process during this test. It employs a rule-based approach to simulate how an agent might react to its environment. This function takes an observation dictionary (obs: dict) as input and returns a decision dictionary. The decision logic prioritizes actions based on the following rules:

Priority 1: Avoid Hazards: The function first checks for nearby hazards. If any hazards are within a critical distance (less than 3.0 units), the agent will prioritize avoiding them. The function iterates through the nearby_hazards list in the observation dictionary. For each hazard, it checks if the hazard's distance is less than the threshold. If a hazard is too close, the function returns a decision to move away from the hazard. The decision dictionary includes the tool (set to "move_away"), parameters (params), and a reasoning string explaining why the decision was made.
Priority 2: Collect Resources: If no immediate hazards are present, the agent will consider collecting resources. The function checks if there are any nearby resources in the observation dictionary. If resources are present, the function determines the closest resource using the min() function and a lambda expression that calculates the distance to each resource. If the closest resource is within a collectable distance (less than 5.0 units), the function returns a decision to move to the resource. The decision dictionary includes the tool (set to "move_to"), parameters (params), and a reasoning string.
Default: Idle: If neither hazards nor collectable resources are present, the agent will default to an idle state. The function returns a decision dictionary with the tool set to "idle", an empty params dictionary, and a reasoning string indicating that no immediate actions are needed. This simple yet effective logic allows us to test the core functionality of the observation-decision loop without the complexities of a more sophisticated AI model. This mock logic allows for controlled testing and debugging of the pipeline itself, ensuring the infrastructure is sound before integrating complex AI decision-making.

def make_mock_decision(obs: dict) -> dict:
    nearby_resources = obs.get("nearby_resources", [])
    nearby_hazards = obs.get("nearby_hazards", [])
    
    # Priority 1: Avoid hazards
    for hazard in nearby_hazards:
        if hazard["distance"] < 3.0:
            return {
                "tool": "move_away",
                "params": {"from_position": hazard["position"]},
                "reasoning": f"Avoiding {hazard['type']} hazard"
            }
    
    # Priority 2: Collect resources
    if nearby_resources:
        closest = min(nearby_resources, key=lambda r: r["distance"])
        if closest["distance"] < 5.0:
            return {
                "tool": "move_to",
                "params": {"target_position": closest["position"]},
                "reasoning": f"Moving to collect {closest['type']}"
            }
    
    # Default: idle
    return {
        "tool": "idle",
        "params": {},
        "reasoning": "No immediate actions needed"
    }

Success Criteria: Defining a Successful Test Run

To ensure that the observation-decision loop test is effective, we need to define clear success criteria. These criteria provide a benchmark against which we can evaluate the test results. A successful test run should meet the following conditions:

/observe endpoint responds to POST requests: This confirms that the backend is correctly configured to receive observation data from the game engine. The endpoint needs to be accessible and able to handle incoming requests.
Mock decision logic returns valid actions: The make_mock_decision() function should produce decisions that adhere to the defined structure, including a valid tool name, parameters, and reasoning. This ensures that the decisions are correctly formatted and can be interpreted by the game engine.
Test scene runs for 10 ticks without errors: The test scene should execute the observation-decision loop for the specified number of ticks without encountering any crashes or exceptions. This indicates the stability of the system during the test.
Each tick sends an observation and receives a decision: The test should successfully transmit an observation to the backend and receive a decision in response for each tick. This validates the communication flow between the game engine and the backend.
Decisions logged to the console with clear formatting: The decisions received from the backend should be logged to the console in a human-readable format, including the tool name, parameters, and reasoning. This facilitates the analysis of the agent's behavior.
Decisions make sense based on the mock game state: The decisions made by the agent should logically align with the mock game environment, such as avoiding hazards and moving towards resources. This verifies the correctness of the decision logic.
No crashes, memory leaks, or hangs: The test should not exhibit any unexpected crashes, memory leaks, or hangs, indicating the overall stability and efficiency of the system. These criteria ensure that the test is not only executed but that its results are reliable and meaningful. Meeting these criteria signifies that the observation-decision loop is functioning correctly and that the agent is making reasonable decisions based on its simulated environment.

Testing Steps: A Practical Guide

To ensure that the observation-decision loop test is conducted correctly, we need to follow a set of well-defined testing steps. These steps provide a practical guide for executing the test and verifying its results.

Start Python IPC server: Before running the test, we need to launch the Python IPC server using the START_IPC_SERVER.bat script. This server hosts the backend logic and the /observe endpoint. Ensuring that the server is running is crucial for the test to communicate with the backend.
Open scenes/tests/test_observation_loop.tscn in Godot: Next, open the test scene in the Godot game engine. This scene contains the test script and the mock game environment.
Press F6 to run: Execute the test scene by pressing the F6 key in Godot. This will start the test script and initiate the observation-decision loop.
Watch the console for 10 ticks of observations and decisions: As the test runs, monitor the console output for the logs of observations sent and decisions received. The output should provide a detailed record of the agent's behavior over the 10 ticks of the test.
Verify decisions match expected behavior: Analyze the decisions logged in the console and ensure that they align with the expected behavior based on the mock game state. The agent should avoid hazards when they are nearby and move towards resources when they are available.
Press Q to quit: Once the test has completed or if you need to terminate it manually, press the 'Q' key. This will trigger the quit functionality in the test script and stop the execution. These steps provide a structured approach to running the test and ensure that all necessary components are in place. By following these steps, testers can reliably evaluate the performance of the observation-decision loop and identify any potential issues.

Expected Console Output: Deciphering the Results

The console output from the observation-decision loop test provides valuable insights into the agent's behavior and the overall functioning of the system. Understanding the expected output format is crucial for interpreting the test results effectively. The expected console output should follow a clear and structured format, providing a comprehensive log of the test execution. The output typically begins with an introduction message indicating the start of the test:

=== Observation-Decision Loop Test ===
Waiting for backend connection...
✓ Connected to backend!

This initial message confirms that the test has started and that the connection to the backend server has been successfully established. If the connection fails, the test will likely terminate with an error message. Next, the output indicates the start of the observation loop:

=== Starting Observation Loop ===
Running 10 ticks...

This message signals that the main loop of the test is beginning and that the agent will process 10 ticks of observations and decisions. For each tick, the output should include the observation data sent to the backend and the decision received in response. The output for each tick typically follows this format:

--- Tick 0 ---
Observation:
  Position: (0, 0, 0)
  Nearby resources: 2
  Nearby hazards: 1
✓ Decision received:
  Tool: move_away
  Reasoning: Avoiding fire hazard

This output shows the tick number, the agent's position, the number of nearby resources, the number of nearby hazards, the tool selected by the agent, and the reasoning behind the decision. The position is represented as (x, y, z) coordinates, and the number of nearby resources and hazards indicates the agent's proximity to these elements in the environment. The decision details, including the tool and reasoning, provide insights into why the agent chose a particular action. This information is crucial for verifying that the agent's decisions align with the mock game state. After all 10 ticks have been processed, the output should indicate the completion of the test:

=== Test Complete ===
All 10 ticks processed successfully!
Press Q to quit

This message confirms that the test has run to completion without encountering any critical errors. If any issues are encountered during the test, error messages will be logged to the console, providing information for debugging. By analyzing the console output, testers can verify that the agent is making reasonable decisions, that the communication between the game engine and the backend is functioning correctly, and that the system is stable and reliable. This detailed output is essential for validating the observation-decision loop and ensuring that it meets the defined success criteria.

Out of Scope: Boundaries of the Current Test

To maintain focus and clarity, it is important to define what is out of scope for the current observation-decision loop test. This helps to prevent scope creep and ensures that the test remains targeted and effective. The current test specifically excludes the following elements:

❌ Actual agent movement execution: The test focuses solely on the decision-making process and does not involve the physical movement of the agent within the game environment. The decisions are logged, but the actions are not executed in the scene. This simplifies the test and allows us to isolate the decision-making logic.
❌ Real LLM integration (using mock logic): The test utilizes mock decision logic instead of integrating with a real Language Model (LLM) backend. This mock logic provides a controlled environment for testing the core functionality of the observation-decision loop. Integrating an LLM is a more complex task and will be addressed in a subsequent phase.
❌ Integration with foraging.gd (separate task): The test is not integrated with the foraging.gd script, which represents a more complete foraging behavior for the agent. This integration will be addressed as a separate task, allowing us to focus on the core observation-decision loop in this test.
❌ Multiple agents: The test is designed for a single agent and does not include support for multiple agents interacting within the environment. Multi-agent support will be added in a future iteration.
❌ Physics/collision: The test does not incorporate physics or collision detection. The mock environment is simplified to focus on the decision-making process. Physics and collision will be integrated in later stages of development. By clearly defining these boundaries, we can ensure that the test remains focused on its core objectives and that the results are relevant and meaningful. This allows us to validate the observation-decision loop in isolation, without the complexities introduced by these additional features. Each of these out-of-scope elements represents a future enhancement that can be built upon the foundation established by this test.

Follow-Up Tasks: Building on Success

Once the observation-decision loop test passes successfully, several follow-up tasks can be undertaken to further enhance the agent's capabilities and the overall system. These tasks build upon the foundation established by the test and pave the way for more complex and intelligent agent behavior. The key follow-up tasks include:

Integrate observation loop into foraging scene: The first step is to integrate the validated observation-decision loop into the foraging.gd script. This will allow the agent to apply its decision-making capabilities within a more complete foraging context. The integration will involve connecting the observation data from the foraging scene to the backend, receiving decisions, and processing those decisions within the foraging behavior.
Replace mock decisions with real LLM backend: The mock decision logic should be replaced with a real Language Model (LLM) backend. This will enable the agent to make more sophisticated decisions based on natural language processing and reasoning. This task will involve setting up communication with the LLM, sending observation data, receiving decisions in natural language, and translating those decisions into actionable commands.
Implement actual movement execution: The decisions made by the agent should be translated into actual movement within the game environment. This task will involve implementing the logic for the agent to move based on the chosen tool and parameters. The agent will need to navigate the environment, avoid obstacles, and interact with resources and hazards.
Add multi-agent support: The system should be extended to support multiple agents interacting within the environment. This will introduce complexities such as communication, cooperation, and competition between agents. Multi-agent support will require modifications to the observation-decision loop, the decision logic, and the movement execution to handle multiple agents concurrently. These follow-up tasks represent a natural progression from the successful completion of the observation-decision loop test. They build upon the validated foundation and progressively enhance the agent's capabilities, paving the way for a more intelligent and interactive game environment. Each task represents a significant step towards realizing the full potential of AI-driven agents in the game.

Estimated Time: A Realistic Timeline

The implementation of the observation-decision loop test has been broken down into phases, each with an estimated time for completion. This allows for a realistic timeline to be established and provides a framework for tracking progress. The estimated time for each phase is as follows:

Phase 1: Backend Endpoint: 30 minutes
Phase 2: Test Scene: 1 hour
Phase 3: Documentation: 30 minutes
Total: ~2 hours

These estimates provide a reasonable expectation for the time required to complete each phase. However, it is important to note that these are estimates and the actual time may vary depending on various factors, such as the developer's familiarity with the codebase, the complexity of the tasks, and any unforeseen issues that may arise. The total estimated time of approximately 2 hours represents a relatively small investment for a test that validates a critical component of the agent's decision-making process. This test ensures that the observation-decision loop is functioning correctly, which is essential for the overall success of the AI-driven agent. By breaking down the implementation into phases and providing estimated times, the development process becomes more manageable and transparent. This allows for effective planning and resource allocation, ensuring that the project stays on track.

Related Files: Navigating the Codebase

To facilitate the implementation of the observation-decision loop test, it is helpful to be aware of the related files within the codebase. These files provide context and guidance for the tasks involved. The key related files include:

scripts/tests/test_tool_execution.gd: This file serves as a reference for the test structure. It provides an example of how to set up a test script, define test cases, and assert expected results. The test_tool_execution.gd script can be used as a template for creating the test_observation_loop.gd script.
scripts/foraging.gd: This file represents a more complete foraging behavior for the agent. While the current test does not integrate with foraging.gd, this file provides context for how the observation-decision loop will be used in a real-world scenario. Understanding the foraging.gd script can help to inform the design of the test and ensure that it aligns with the agent's overall goals.
python/ipc/server.py: This file contains the backend server logic, including the /observe endpoint that needs to be modified. This file is central to Phase 1 of the implementation, which involves adding the endpoint and implementing the mock decision logic. Familiarizing oneself with the structure and functionality of python/ipc/server.py is essential for successfully completing this phase.
python/tools/movement.py: This file provides example tool implementations. These examples can be used as a reference for defining the tools that the agent will use to interact with the environment. Understanding the existing tool implementations can help in designing the mock decision logic and ensuring that the agent's decisions are compatible with the available tools. By being aware of these related files, developers can navigate the codebase more effectively and efficiently. This knowledge facilitates the implementation of the observation-decision loop test and ensures that it aligns with the existing system architecture.

In conclusion, implementing the observation-decision loop test is a crucial step in validating the end-to-end pipeline of an AI-driven agent. This test ensures that the agent can effectively perceive its environment, make informed decisions, and initiate actions accordingly. By following the phased approach outlined in this article, developers can systematically implement and test this vital functionality, paving the way for more intelligent and interactive game environments. For more information on AI and game development, check out this great resource.