Gymnasium
6502_65C02_functional_tests
Gymnasium | 6502_65C02_functional_tests | |
---|---|---|
12 | 7 | |
5,859 | 364 | |
6.8% | - | |
9.3 | 0.0 | |
12 days ago | about 1 year ago | |
Python | ||
MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Gymnasium
-
NASA JPL Open Source Rover That Runs ROS 2
"Show HN: Ghidra Plays Mario" (2023) https://news.ycombinator.com/item?id=37475761 :
[RL, MuZero reduxxxx ]
> Farama-Foundation/Gymnasium is a fork of OpenAI/gym and it has support for additional Environments like MuJoCo: https://github.com/Farama-Foundation/Gymnasium#environments
> Farama-Foundatiom/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium
-
Show HN: Ghidra Plays Mario
https://github.com/Farama-Foundation/Gymnasium#environments
Farama-Foundatiom/MO-Gymnasiun:
-
Are there any AI projects that plays a game for you and learns?
https://github.com/Farama-Foundation/Gymnasium - A framework Python library to build and train your own AI to play games
-
Unstable SAC training of sparse-reward task
The only change in the environment from the one here is the reward function which is given its return value using the following code snippet (replacing lines 648-672 in the above url):
-
Any resources on experiments simulated environments?
This may be useful: https://github.com/Farama-Foundation/Gymnasium
-
What's the most challenging Gym environment?
Here are all the environments. So for example, if instead of Hopper-v2 you want the acrobat environment from classic control you can write: env = gym.make('Acrobot-v1')
-
Gymnasium 0.28 is now released
This release also includes a large number of documentation updates, minor bug fixes, and other minor improvements; the full release notes are available here if you’d like to learn more: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.28.0.
-
TransformerXL + PPO Baseline + MemoryGym
Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.
- Gymnasium 0.27 - the first new version since Gymnasium was announced - is now released. It has almost no breaking changes.
-
[N] Gymnasium 0.27 - the first new version since Gymnasium was announced - is now released. It has almost no breaking changes.
You can read the release notes here: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.27.0. You can upgrade from 0.26 without any changes unless you're doing something very uncommon; this is how releases will generally be going forward.
6502_65C02_functional_tests
-
Show HN: Ghidra Plays Mario
Klaus Dormann's 6502 tests don't rely on a particular emulator environment. They could be used with Ghidra.
https://github.com/Klaus2m5/6502_65C02_functional_tests
-
How do I tell if my 65c02 is bad?
How about some assembler code to test all of the opcodes? https://github.com/klaus2m5/6502_65c02_functional_tests
-
I made a cycle accurate profiler for 65C02 assembly with visualizations
https://github.com/Klaus2m5/6502_65C02_functional_tests might be worth a look, it's a comprehensive test suite
-
What's the address of the monitor disassembly routine?
Great! (and not surprising). You may want to look into using a 6520 test suite to check correctness of your emulator, like this one -- note: I have no experience with it, but it took me some time to iron out the last error of my 6502 emulator, and in hindsight I should probably have used such test suite.
- Built a 65C02 emulator
-
Test - Corner cases for 6502 Instructions.
Currently i'm trying to implement 6502's instructions one by one using TDD. I was curious are there any test - corner cases already been written ? I found out ( https://github.com/Klaus2m5/6502_65C02_functional_tests ) but this requires all instructions to be implemented which I don't currently. Is there any way to test a single instruction in isolation for all the edge cases ?
-
Apple //e enhanced ROM oddness
By "bad branch", I mean the emulator takes the wrong branch because it fails to emulate some part of the Apple hardware properly. The 65C02 emulation has passed some pretty stringent tests (https://github.com/Klaus2m5/6502_65C02_functional_tests/blob/master/bin_files/65C02_extended_opcodes_test.lst), so I'm pretty confident in it. But the instruction trace file is around 90,000 lines, so is kinda hard to slog through.
What are some alternatives?
flake8 - The official GitHub mirror of https://gitlab.com/pycqa/flake8
retro - Retro Games in Gym
Flake8-pyproject - Flake8 plug-in loading the configuration from pyproject.toml
MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning
ruff - An extremely fast Python linter and code formatter, written in Rust.
ghidra-plays-mario - Playing NES ROMs with Ghidra's PCode Emulator
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
ghidra-tlcs900h - Ghidra processor module for Toshiba TLCS-900/H
Visual Studio Code - Visual Studio Code
episodic-transformer-memory-ppo - Clean baseline implementation of PPO using an episodic TransformerXL memory
flake8
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.