changed readme

Author: drblallo

README.md

@@ -16,6 +16,8 @@ Rulebook:
 
 Our key proof of concept example is [4Hammer](https://github.com/rl-language/4Hammer) . A never before implemented reinforcement learning environment with huge amounts of user actions in only ~5k lines of code (including graphical code). It runs in the browser and on desktop and all the features described in this section are present.
 
+Zero mallocs unless explicitly requested by the user.
+
 ### Installation
 
 Install rlc with:
@@ -46,20 +48,8 @@ rlc-probs file.rl net
 ```
 It will to learn pass true to `win` to maximize `score`, as reported by the second command.
 
-##
-### Documentation
-[Project Rationale](./docs/where_we_are_going.md)
-
-[Language Rationale](./docs/rationale.md)
-
-[Tutorial](./docs/tutorial.md)
-
-[Tutorial for GYM users](./docs/gym_tutorial.md)
-
 [Paper for Reinforcement Learning users](https://arxiv.org/abs/2504.19625)
 
-[Language reference and stdlib documentation](https://github.com/rl-language/rlc-stdlib-doc/tree/master)
-
 ### Contacts
 
 [Discord](https://discord.gg/saSEj9PAt3)
@@ -71,224 +61,11 @@ Or mail us at massimo.fioravanti@polimi.it
 
 ![RLC Logo](./imgs/RLC_logo.png)
 
-### Example: tic tac toe
-```
-
-# declares the equivalent of a struct called Board.
-# It contains the tic tac toe slots and the current player turn
-# Methods omitted for brevity
-cls Board:
-	Int[9] slots
-	Bool playerTurn
-
-@classes
-act play() -> TicTacToe:
-	# allocates and initializes a board of type Board
-	let board : Board
-	while !full(board):
-
-		# declares a suspension point of the simulation,
-		# an action called mark that requires two ints to be performed.
-		act mark(Int x, Int y) {
-		# declares contraints about which inputs are valid
-			x < 3,
-			x >= 0,
-			y < 3,
-			y >= 0,
-			board.get(x, y) == 0
-		}
-
-		# marks the board at the position provided
-		board.set(x, y)
-
-		# if the current player has three marks in a line
-		# return
-		if board.three_in_a_line():
-			return
-
-		board.change_current_player()
-
-
-fun main() -> Int:
-	# creates a new game
-	let game = play()
-	game.mark(0, 0)
-	# X _ _
-	# _ _ _
-	# _ _ _
-	game.mark(1, 0)
-	# X O _
-	# _ _ _
-	# _ _ _
-	game.mark(1, 1)
-	# X O _
-	# _ X _
-	# _ _ _
-	game.mark(2, 0)
-	# X O O
-	# _ X _
-	# _ _ _
-	game.mark(2, 2)
-	# X O O
-	# _ X _
-	# _ _ X
-
-	# returns 1 because player 1 indeed
-	# had three marks in a line
-	return int(game.board.three_in_a_line())
-```
-
-### FAQ:
-#### I am a reinforcement learning engineer, what do I gain from using this?
-By using RLC to write your environments, or to wrap previously existing environments, you obtain:
-* the ability of automatically test those environments.
-* configurable automatic serialization and deserialization textual and binary for those environments.
-* configurable automatic serialization and deserialization textual and binary for sequences of actions instead of the state.
-* configurable automatic serialization of the state to something that can be sent to the GPU for learning.
-* the ability to reuse the environment code of the environment in production with no modification.
-
-You can read more about the tutorial here [Tutorial](./docs/tutorial.md).
-
-#### I am a graphic engine programmer/game programmer, what do I gain from using this?
-By writing state and state evolution code (not graphical code) in Rulebook you obtain:
-* the ability of automatically serialize the state to disk both in textual and binary form.
-* the ability to automatically test and stress code witouth running the whole engine and thus testing it in isolation.
-* the ability to reuse state code indipendetly from the engine.
-* retain the ability of writing graphical code however you wish.
-
-You can checkout a example where RLC is made interoperable with Godot [here](https://github.com/rl-language/4Hammer).
-
-#### I can write the same tic tac toe example in python using python yields, what is the difference?
-The difference is that when written in python:
-* python coroutines lack a mechanism to express multiple possible resumption points.
-* python coroutines allocate the coroutine state on the heap, RLC does not.
-* you lose the ability to serialize and restore the execution of tic tac toe between player actions.
-* you must use some special convention to extract the state of the board from the active coroutine, such as saving the reference to the board somewhere else.
-* you must use special convention must be followed to express somewhere which values of x and y are valid and which are not, and such requirements cannot be expressed inline in the coroutine, defeating the advantage of using the coroutine.
-* you must manually specify how to encode the suspended coroutine to something that can be delivered to machine learning components.
-
-RLC does all of this automatically. You can read more about it [Here](./docs/rationale.md).
-
-
-#### I have a previously existing code base, can I use this project?
-Yes, at the moment Rulebook is compatible with python and C. You can use RLC as build only tool for testing purposes and not affect in any way your users.
-
-#### I have performance constraints, is this fast?
-We have performances comparable with C. Furthermore you can write C code and invoke it from Rulebook if you need ever more controll on performances.
-
-#### In practice, what happens to a project that wants to include Rulebook components?
-Everything about Rulebook will be turned into a single native library that you will link into or deploy along with your previously existing artifacts. Nothing else.
-
-
-```
----------- 0 : p0 ------------
-{resume_index: 1, score: 0.000000}
---------- probs --------------
-0: win {do_it: true}  98.9385 %
-1: win {do_it: false}  1.0615 %
-------------------------------
-{resume_index: -1, score: 1.000000}
-```
-
-Read a tutorial explaining how to play black jack [here](./docs/tutorial.md)
-
 ### License
 
 We wish for `RLC` to be usable by all as a compiler, for both commercial and non-commercial purposes, so it is released under apache license.
 
 
-## Info for compiler developers.
-This section is dedicated to those that whish to build RLC itself from source, not to those that wish to use RLC as a off-the-shelf tool. At the moment we do not provide a off-the-shelf way of building RLC on windows due.
-
-### Dependencies
-Base:
-* cpp17 compiler
-* python
-* CMake
-
-Extra dependecies used by the setup script:
-* Bash
-* Ninja
-* virtualenv
-* lld
-
-
-### Installation for compiler developers
-Stop reading if you don't want to work on the compiler.
-
-We provide a setup script that downloads the rlc repository and a setup script that will download and compile `LLVM` as well as `RLC`. As long as the dependencies written before are met you should just be able to run the following commands and everything should work. Installing and building llvm debug will take ~100 gigabytes of hard drive space and will require a large amount of time and RAM. This is only required when building from sources, pypi packages are less than 100mb on each OS.
-
-Hard drive space can be reclaimed by deleting `LLVM` build directory after it has been fully built.
-
-Download the setup.sh file in the root of the repository and then run:
-```
-chmod +x setup.sh
-source ./setup.sh # clones RLC repo and initialize virtualenvs and submodules
-python rlc/build.py # clones LLVM, builds it and builds RLC
-```
-
-on mac and windows replace the last line with
-```
-python rlc/build.py --no-use-lld
-```
-
-If that script terminates successfully, you are fully set up to start working on `RLC`.
-
-
-#### Building the pip packages
-
-You can create a pip package in BUILDDIR/dist/ by running
-```
-ninja pip_package
-```
-
-#### What do if run out of space or memory
-Instead of the previous command python, you can run. This will only build the release LLVM version and save a great deal of space.
-```
-python rlc/build.py --no-debug-llvm
-```
-
-#### Using a custom LLVM
-```
-python rlc/build.py --llvm-dir <PATH-TO-LLVM-INSTALL> [--rlc-shared]
-```
-
-You need to use the flag --rlc-shared if you have built a shared LLVM.
-
-### environment.sh
-If you are using the default installation script (setup.sh) we provide a .sh file that configures your environment variable so that you can use python and rlc without installing anything in your actual machine.
-When you open a shell to start working on RLC run the following command.
-
-If you use some editor such as code or clion, start it from that shell.
-
-```
-source environment.sh
-```
-
-To check if everything works correctly run the following command.
-```
-python python/solve.py ./tool/rlc/test/tic_tac_toe.rl
-```
-If it does not crashes, then you are good to go.
-
-If you use some whacky shell of your own or you did not followed the default setup, you are on your own.
-
-
-### How to contribute for developers
-* fork this project.
-* push your branches to your fork
-* open a pull request for the branch relevant to your project
-
-The intent of this workflow is so that reviewrs can use the review feature of github pull requests to have persistent comment threads.
-
-### Roadmap for 1.0
-
-#### language
-* better debug support
-* inline initializers
-* better cast guards
 
-#### standard lib
-* dictionary library
 
 

python/setup.py

@@ -134,7 +134,6 @@ def copy_binaries(source_directory, destination_directory):
             "rlc-play=impl.play:main",
             "rlc-random=impl.solve:main",
             "rlc-probs=impl.probs:main",
-            "rlc-fix-ray=impl.fix_ray:main",
             "rlc-make-report=impl.make_report:main",
             "rlc-llmplayer=impl.llmplayer:main",
         ],