Muzero-unplugged
6502_65C02_functional_tests
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| GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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Muzero-unplugged
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Show HN: Ghidra Plays Mario
https://github.com/DHDev0/Muzero-unplugged
Gym is now gymnasium and it has support for additional Environments like Mujoco:
- Implementation of MuZero, MuZero Unplugged and Stochastic MuZero
6502_65C02_functional_tests
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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
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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
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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
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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
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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 ?
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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?
Stochastic-muzero - Pytorch Implementation of Stochastic MuZero for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
retro - Retro Games in Gym
Muzero - Pytorch Implementation of MuZero for gym environment. It support any Discrete , Box and Box2D configuration for the action space and observation space.
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
pytorch-A3C - Simple A3C implementation with pytorch + multiprocessing
MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning
nn-morse - Decode morse using a neural network
ghidra-plays-mario - Playing NES ROMs with Ghidra's PCode Emulator
ghidra-tlcs900h - Ghidra processor module for Toshiba TLCS-900/H
neural-network-scratch - build a neural network to show as a demonstration on inner workings of a neural network