Open Issues Need Help
View All on GitHubA JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Create a comprehensive guide to Git basics for contributors to the CuMind project. The guide should cover branching, committing, amending, merging, pushing, and best practices, with clear explanations, examples, and a focus on beginner-friendliness.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Create a beginner-friendly guide to essential Linux commands, system updates, `sudo`, and `apt` for contributors to the CuMind MuZero RL project. The guide should include clear explanations, practical examples, and be easy to follow for users with little to no Linux experience.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement push path ignore filters ('.md', 'docs/', 'extras/**') in the project's CI/CD workflows to prevent unnecessary workflow triggers from pushes to documentation and extra files. This involves investigating the current workflow configuration, updating it with the specified ignore patterns, and validating the changes.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Create comprehensive onboarding documentation for setting up the development environment for the CuMind project. This includes instructions for setting up WSL on Windows, configuring a WSL Ubuntu environment, setting up VSCode or Cursor, configuring GitHub authentication (SSH or HTTP), and utilizing the GitHub Student Pack. The documentation should be in markdown format and allow users to successfully clone and deploy CuMind locally.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Document the tooling and ecosystem for the CuMind project's documentation repository, including quality control (pytest, mypy, ruff), shared modules architecture, meeting schedules, communication protocols, Git workflow, and best practices. Provide practical examples and quick-start guides.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the CuMind MuZero implementation with Atari environments from Gymnasium, document the process, record results and metrics, and propose any necessary follow-up actions. This involves setting up the environment, adapting the CuMind agent to handle Atari's image-based observations, running experiments, and analyzing the performance.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the CuMind MuZero implementation with MuJoCo environments from Gymnasium, documenting the process, challenges, results, and proposing any necessary follow-up actions. This involves setting up the environment, running CuMind within it, recording performance metrics, and documenting the entire process.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the CuMind MuZero implementation with Gymnasium's Toy Text environments, document the process, record results and metrics, and propose any necessary follow-up actions. This involves adapting the CuMind configuration and potentially handling any environment-specific challenges.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the CuMind MuZero implementation with Box2D environments from Gymnasium. This involves setting up the environment, adapting CuMind's configuration, running experiments, documenting the process, and reporting results and metrics. Challenges encountered during integration should also be documented.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the CuMind MuZero RL algorithm implementation with classic control environments from Gymnasium, document the process, record results and metrics, and propose any necessary follow-up actions. This involves setting up the environment, adapting CuMind as needed, running experiments, and analyzing the performance.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the CuMind MuZero implementation with various Gymnasium environments (Classic Control, Box2D, Toy Text, MuJoCo, Atari, and External Environments). This involves creating sub-issues for each environment category, documenting the integration process, addressing challenges, and demonstrating successful results with metrics.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement proper versioning for the CuMind project using a chosen versioning strategy (e.g., setuptools_scm, versioneer), including setting up version management tooling, creating a version bumping workflow, and documenting the process. Consider the implications of versioning in a monorepo and synchronize versions across components if necessary. Research best practices for Python package versioning and semantic versioning (SemVer).
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Update the project's onboarding documentation to reflect its mono-repo structure. This involves creating comprehensive documentation across three categories: Development Environment Setup, Software Engineering Process, and Tooling and Ecosystem. The documentation should be clear, well-organized, and include practical examples and step-by-step guides, similar in style to ReadTheDocs.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate the @google-deepmind/mctx library into the CuMind project, replacing the existing manual Monte Carlo Tree Search (MCTS) implementation. This involves adding mctx as a dependency, refactoring the code to use the library's API, updating tests, and creating comprehensive documentation for the integration.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: The task involves researching and implementing a distribution strategy for the CuMind MuZero RL algorithm, including publishing to PyPI, exploring alternative methods like self-hosting or GitHub releases, and integrating CI/CD for automated releases. This includes setting up authentication, versioning, and creating comprehensive documentation.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement comprehensive integration tests for a MuZero reinforcement learning algorithm implementation, covering training convergence, memory management, and checkpoint recovery. These tests should run within a reasonable timeframe and be integrated into the CI/CD pipeline.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement a comprehensive evaluation and benchmarking suite for the CuMind MuZero RL agent, including CLI commands, visualization tools, standardized metrics (like episode rewards, lengths, action entropy, etc.), checkpoint comparison, and a human play interface. The suite should generate reports (HTML/PDF) and support various environments.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement value and reward transformation using a categorical representation in a JAX/Flax based MuZero implementation. This involves adding configuration parameters, creating transformation functions, updating the network architecture and loss computation, modifying the MCTS algorithm, and adding tests and documentation. The goal is to improve training stability and performance, particularly in environments with large or unbounded rewards.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement TensorBoard and Weights & Biases logging for a MuZero reinforcement learning agent in JAX, tracking various training and MCTS metrics. This involves creating an abstract logger interface, specific implementations for each logging backend, integrating them into the training and evaluation loops, and adding CLI flags for configuration.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Create five interactive Jupyter notebooks demonstrating the CuMind MuZero RL algorithm. These notebooks should cover quickstart, MCTS visualization, custom environments, hyperparameter tuning, and advanced features. Visualization functions will need to be implemented, and the notebooks should be well-documented and runnable in both Jupyter and Colab environments.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement a GitHub Actions CI/CD pipeline for the CuMind MuZero RL project. This involves creating a workflow to automate linting, type checking, testing (on multiple Python versions), and build verification. The pipeline should prevent merging of pull requests with failing checks and include build status badges in the README.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Enhance the documentation for the CuMind project by adding comprehensive instructions and example Jupyter notebooks on how to integrate and utilize custom reinforcement learning environments. This involves creating a README file within the `src/environments` directory and at least one example notebook in the `notebooks` directory, both demonstrating the complete workflow of registering and training with a custom environment. The documentation should follow the style of Gymnasium/gymnax.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Resolve or suppress Pygame deprecation warnings within the CuMind MuZero RL project, focusing on warnings triggered by Gymnasium's human rendering mode. This involves identifying the warning sources, proposing and implementing fixes or workarounds, and ensuring forward compatibility. Documentation of any unresolved warnings is also required.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Review the current import styles (local filesystem vs. package imports) in the CuMind project, document the pros and cons of each approach, and provide recommendations for best practices and next steps. This includes creating documentation outlining the findings and recommendations for migrating to a consistent style or maintaining the status quo.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Apply and document JAX GPU performance optimization tips to the CuMind MuZero RL implementation. This involves researching and implementing relevant optimizations, then documenting the process and results within the project's documentation.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Conduct a code audit of the CuMind JAX-based MuZero implementation to identify and document any unintended side effects stemming from non-functional programming practices. Propose solutions to mitigate these side effects and ensure the code adheres to functional programming principles.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Improve the project's README, onboarding documentation, and contributing guidelines to make it easier for new contributors to get started. This includes updating existing documentation and potentially migrating to a documentation tool like mkdocs.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Standardize top-of-file docstrings for all modules in the CuMind MuZero RL project to improve code readability and maintain documentation consistency. This involves defining a docstring standard and then refactoring the codebase to comply with it. Guidelines for the standard should also be documented.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Investigate and document GPU profiling tools (like NVIDIA Nsight) for the MuZero RL algorithm implemented in JAX, benchmark alternatives, identify performance bottlenecks, and generate actionable reports. This involves developing a profiling workflow and creating reports detailing performance hotspots.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Refactor the MuZero RL algorithm implementation in JAX to enforce data type (dtype) and other configurable variables solely through a central configuration file. This involves auditing the codebase, modifying it to exclusively use configuration values, and documenting the changes. The goal is to ensure a single source of truth for all configurable parameters.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Thoroughly evaluate the `treebuffer` class within the CuMind MuZero RL project. This involves testing its correctness in both synthetic and real-world environments, benchmarking its performance against alternative memory buffer implementations, and finally, producing a comprehensive report summarizing the findings and providing recommendations for its adoption or potential improvements.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Integrate Weights & Biases (wandb) and/or TensorBoard logging into an existing MuZero RL algorithm implementation (CuMind) written in JAX. This involves adding hooks for logging, providing CLI/config options to enable/disable logging, and updating documentation with examples.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Audit the `config.json` and `config.py` files in the CuMind project to ensure all configuration options, particularly data types (e.g., bfloat16, float32, int8), are present and used consistently throughout the codebase. Update documentation as needed.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Refactor the MuZero JAX implementation to ensure all code in the `src/` directory passes mypy type checking and integrate the `chex` library for robust JAX testing. This involves addressing any type errors identified by mypy and writing or extending tests using `chex` to cover core JAX code paths.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.
AI Summary: Implement a Textual User Interface (TUI) for the CuMind MuZero RL project, allowing users to monitor and manage jobs more effectively than the current command-line interface. This involves researching and prototyping TUI frameworks like Textualize or rich, focusing on features for displaying job status and details and integrating with the existing workflow.
A JAX-based CuMind is a JAX-based RL framework inspired by Google DeepMind. It combines Monte Carlo Tree Search (MCTS) with a learned model to achieve superhuman performance in complex domains without prior knowledge of their rules.