This milestone focuses on creating the foundational environment required to implement a hybrid quantum-classical reinforcement learning (QRL) agent. The work involves integrating quantum computing frameworks such as Qiskit, PennyLane, or Cirq with standard reinforcement learning libraries like OpenAI Gym or PettingZoo. We will implement quantum circuits for Q-value approximation and set up an environment where agents can be trained using quantum-enhanced Q-learning algorithms. Special attention will be given to ensuring reproducibility, extensibility, and compatibility with classical baselines. The scope also includes setting up continuous integration (CI) pipelines and automated test scripts to ensure high code quality. The goal is to produce a minimal viable prototype of a quantum agent that can solve basic tasks like CartPole, FrozenLake, or GridWorld with measurable learning outcomes.

* Fully implemented QRL environment using a hybrid quantum-classical architecture * Custom Q-learning agent leveraging variational quantum circuits for Q-value estimation * Benchmark environments: CartPole, FrozenLake, and a discrete GridWorld for evaluation * Docker container or Colab notebook for reproducible setup * Technical documentation covering system architecture, circuit design, and agent logic * Test suite for quantum circuit validity and learning performance consistency * Performance comparison between the quantum and classical Q-learning agent for simple tasks * Codebase uploaded to a public GitHub repository under an open-source license (e.g., MIT or Apache 2.0)

$10,000 USD

* A functional quantum-enhanced Q-learning agent capable of converging to a policy on at least two environments * Reproducible environment that can be set up and run using provided documentation and containers * Performance metrics showing that the quantum agent learns within a reasonable number of episodes compared to classical baselines * All quantum circuits used in the agent have passed validation tests (e.g., unitarity, fidelity) * Repository is well-organized and documented with a clear README, setup instructions, and architecture diagram * At least one contributor outside the team forks or stars the repository during this phase, indicating early interest or engagement

This milestone is dedicated to building the cognitive-symbolic interface that enables high-level symbolic goals to influence the learning trajectory of the QRL agent. We will leverage existing cognitive architectures such as MeTTa (from OpenCog) or Hyperon to encode goals in a symbolic form. These goals will be translated into low-level control signals or reward modifications for the QRL agent. The interface layer will act as a “translator” that maps symbolic intents into actionable subgoals or reward-shaping heuristics, thus creating a hybrid learning framework combining symbolic cognition with quantum-enhanced decision-making. This milestone also includes experimenting with symbolic reasoning to dynamically adjust the exploration/exploitation balance or modify learning rates. Key challenges addressed will include goal grounding, ontology parsing, and interfacing between symbolic and numerical representations.

* Implementation of a cognitive-symbolic interface that takes symbolic expressions (e.g., “maximize safety,” “prefer short paths”) and translates them into reward functions or learning directives * Integration of MeTTa or Hyperon symbolic reasoning systems with the QRL backend via a defined API * Test scenarios where symbolic instructions guide the QRL agent through navigation or choice-based environments * Ontology parser or symbolic-to-reward translation engine * Demo showing how the same environment behaves differently when controlled by agents with different symbolic goal profiles * Documentation outlining the symbolic goal formulation syntax, internal translation logic, and agent integration * Codebase extended to support pluggable symbolic backends, allowing future experiments with other symbolic tools.

$10,000 USD

* Symbolic goals must measurably influence agent behavior (e.g., choosing safer but longer paths) across at least three test environments * QRL agents with cognitive-symbolic feedback should show improved convergence in sparse-reward settings compared to agents without symbolic input * Successful parsing and application of symbolic instructions in real-time, with minimal latency (<100ms translation time) * Interface design must be modular, extensible, and able to support at least two symbolic backends * All interfaces are fully documented with usage examples and visual schema * Completion of a public video demo or interactive walkthrough of symbolic-to-QRL interaction * Feedback from at least one cognitive computing expert or symbolic AI researcher validating the correctness of symbolic integration

This milestone focuses on a thorough performance evaluation of the hybrid QRL agent with symbolic feedback compared to classical reinforcement learning agents. We will design controlled experiments across diverse environment types: navigation, decision-making under uncertainty, and multi-agent coordination. Evaluation will cover sample efficiency, convergence rate, robustness to noise, and generalizability. We will also analyze computational costs associated with quantum circuit evaluation and symbolic processing overhead. This phase includes hyperparameter optimization, ablation studies to isolate contributions of the quantum and symbolic components, and exploratory analysis of emergent behaviors when combining both paradigms. Insights will be used to fine-tune the architecture for improved learning performance and system responsiveness.

* Full evaluation pipeline using tools like Weights & Biases or TensorBoard * Comparative performance charts showing hybrid vs classical agent results on at least five environments * Hyperparameter tuning results (learning rate, quantum circuit depth, symbolic goal complexity) * Ablation study results identifying the contributions of the quantum and symbolic components independently * Tables and plots summarizing key findings (e.g., average reward, variance, training time) * Technical report describing evaluation methodology, experiment setup, and statistical analyses * Published notebook or dashboard with interactive performance visualizations * Recommendations for improving scalability and adaptability of the hybrid agent

$10,000 USD

* Hybrid agent demonstrates statistically significant improvements (p < 0.05) in at least two metrics (e.g., sample efficiency, convergence speed) over classical RL agents * At least one environment where symbolic-augmented QRL agent outperforms both classical and non-symbolic QRL agents * Computation overhead from quantum and symbolic components is quantified and remains within practical limits * Ablation study shows clear individual benefits of quantum and symbolic modules * Evaluation code and data are open-sourced and reproducible by third parties * Engagement from academic or research community (e.g., citation, GitHub discussion, or feedback request) validating interest and scientific merit.

The final milestone delivers a public-facing demonstration and comprehensive documentation of the full system. This includes building a web-based or video-guided demo that illustrates how the QRL agent with symbolic feedback works across selected tasks. We will produce detailed technical documentation, open-source the entire codebase, and release guides for replicating or extending the system. Community engagement will be a priority: we'll write explainer blog posts, contribute to related forums (e.g., LessWrong, OpenCog), and host a livestream or webinar. The goal is to transition the project from prototype to a well-documented, reusable framework that researchers and developers can build on.

* Interactive demo (e.g., Streamlit, GitHub Pages, or hosted Jupyter Notebook) showcasing key capabilities * Recorded video walkthrough highlighting architecture, symbolic interface, and performance gains * Full technical documentation including setup guide, architecture diagrams, API references, and glossary * Blog post or explainer article aimed at both technical and non-technical audiences * GitHub repository including full code, example notebooks, and contribution guidelines * Public presentation of the project (recorded session or live Q&A with the Deep Funding community) * Social media or newsletter announcement with clear call to action for contributors and collaborators.

$10,000 USD

* All documentation is peer-reviewed for clarity and completeness by at least one external reviewer * Public code repository is starred or forked by at least five users, signaling community interest * One or more contributors outside the team submit an issue, pull request, or extension idea * Project receives positive feedback from at least one expert or community member through blog, forum, or email * Team hosts or participates in a public webinar/podcast/interview related to the project launch * Code and documentation enable successful reproduction of at least one agent behavior by an unaffiliated party.