Download v1.1 (64-bit version)Download v1.1 (64-bit version)Orion 1.1 is now available!
After devoting a lot of time to Nostradamus, a chess engine that uses a large language model to learn and predict the optimal move for a given position, I finally found the time to enhance Orion!
Here is the new version of 'my little engine':
The gain in Elo should be quite small (something around 50+ Elo), but consistent in my tests with three different datasets mixing CCRL and/or CEGT games. However, I only had time to test at short time controls, so fingers crossed!
Most of the time for this release has been spent on the Cerebrum library. The library has been adapted to focus only on quantised networks, and predict values beyond the previous limited range of -1.98 to 1.98. This allows the networks to predict evaluations directly in terms of pawns and centipawns for direct use in engines. There is no need to mix different values on different scales (win ratio and material) anymore!
I discovered NotebookLM from Google last summer, and used it to generate a podcast from this blog. It's astonishing!
The Lal Kitab Amrit Vashist Jyotish is a modernized astrological system developed by Gurudev GD Vashist that integrates traditional Lal Kitab principles with specialized Vashist Jyotish calculations
. The updated version, often referred to as the "New Lal Kitab Amrit" or " Yug Parivartan Kundli
," incorporates new research—including formulas from Muni Agastya—to provide more detailed life predictions and remedial measures . Key Features of the Updated Edition
72-Year Life Roadmap: Unlike standard horoscopes, this version provides a comprehensive life account covering birth to 72 years, broken down into four-month intervals .
Personalized Remedies: Offers specific solutions for health, wealth, relationships, and career based on planetary positions and "Pitra Rin" (parental debt) .
Astro-Palmistry: Combines the study of palm lines with birth chart analysis to confirm planetary influences .
Precise Timing: Uses a combination of Lal Kitab Dasha and Varshphal to identify auspicious and unfavorable periods for major life events like marriage or business ventures . How to Access the Content
The updated material is available in several formats through official channels like Astroscience: Accurate Solutions in Lal Kitab By Gurudev Gd Vashist
The Lal Kitab Amrit Vashist Jyotish is an advanced evolution of traditional Lal Kitab astrology, modernized by Gurudev G.D. Vashist to provide high-precision life guidance through personalized PDF files or physical books. Unlike conventional Vedic systems that focus heavily on zodiac signs, this methodology prioritizes planetary house placements and practical, "common-sense" remedies that do not require complex rituals. Core Philosophy and Modern Updates
The "Amrit" version integrates traditional wisdom with modern research and software-driven accuracy. It is often delivered as a comprehensive personalized Kundli file tailored to an individual’s birth year.
Vashist Principles: It emphasizes house-based analysis and the ascendant (rising sign) to ensure the most accurate predictions.
Predictive Clarity: The updated version aims to eliminate "guessing" by providing clear dates for significant events like marriage or career shifts.
Holistic Integration: It combines Lal Kitab with elements of palmistry and other astrological divisions to create a "Vashist Jyotish" calculation. Key Features of the Updated PDF Report
When you download or purchase an updated Lal Kitab Amrit Vashist Jyotish file, it typically includes:
Detailed Kundli Analysis: A deep dive into planetary positions and their specific impacts on health, wealth, and relationships.
Pitra Rin (Parental Debt): Identification of karmic debts that may be blocking progress, along with specific corrective measures.
Ishtdev & Auspicious Periods: Guidance on your favorable deity and identifying "safe" or "dangerous" times for major life decisions.
Personalized Remedies: Practical tasks—such as offering specific items to nature or wearing certain gemstones—designed to mitigate negative planetary influences. lal kitab amrit vashist jyotish pdf updated
Annual Horoscope (Varshphal): A roadmap for the coming year, outlining predicted challenges and opportunities. Learning and Resources
For those interested in studying the system themselves, Gurudev G.D. Vashist offers a set of three books: lal-kitab-amrit-software-gd-vashist-free-download - Scribd
Downloading the PDF is just the first step. Here is how to extract real value from Lal Kitab Amrit:
This is the most popular section in the updated PDF:
Early printings of Lal Kitab had significant typographical errors—especially in planetary degrees and remedy procedures. The updated version corrects mantra spellings, remedy ingredients, and house rules.
Before diving into the PDF, understand why this system is revolutionary. Unlike traditional Jyotish:
| Feature | Standard Parashari Jyotish | Lal Kitab (via Amrit Vashist) | | :--- | :--- | :--- | | Calculations | Complex divisions (Shodashvarga) & planetary periods (Dasha) | Simple 12-house chart based on Moon sign | | Focus | Prediction of events | Prescription of remedies to change destiny | | Remedies | Expensive: gemstones, fire rituals (Havans) | Low-cost: food, charity, odd objects, daily actions | | Planets | Fixed nature (e.g., Saturn always malefic in certain houses) | Planets change behavior based on house position & inter-relationships | | Karmic View | Past life debts (Prarabdha) are fixed | Debts can be "written off" via simple acts like feeding crows or dogs |
Lal Kitab Amrit emphasizes that your horoscope is a map of your debts (Rin) and past actions. The remedies are designed to repay these debts quickly.
If you are looking for the "Lal Kitab Amrit" PDF, you are likely looking for a version that solves the riddles of the original Red Book.
Why this version is preferred:
Note: While digital PDFs are widely shared on astrology forums and groups, ensure the file is complete (often running over 400+ pages) to guarantee it includes the remedy sections, as some abridged versions cut out the practical "Upay" chapters.
Lal Kitab Amrit Vashist Jyotish is a specialized branch of Vedic astrology that combines traditional Lal Kitab remedies with the unique "Vashist Method" developed by Pt. G.D. Vashist. This updated system focuses on the concept of "Nishulk Upay" (easy remedies) and the analysis of planetary effects on modern life. Core Principles of the Updated System
Simplified Remedies: Focuses on practical, everyday objects to balance planetary energies.
Modern Interpretation: Adapts ancient 19th-century verses to 21st-century lifestyles.
Visual Charts: Uses the "Lal Kitab Teva" (a fixed-house horoscope) for diagnosis.
Debt Analysis: Emphasizes "Pitra Rin" (ancestral debts) and specific ways to clear them. What the Updated PDF Usually Includes
Comprehensive Predictions: Detailed analysis for health, wealth, and career. The Lal Kitab Amrit Vashist Jyotish is a
The 35-Year Cycle: A breakdown of the Vashphal (yearly predictions).
Precise Precautions: Lists of "don'ts" (e.g., items not to keep at home) to avoid bad luck.
Logic-Based Astrology: Explanations of why a remedy works based on planetary logic. 💡 Key Highlights
No Rituals: Most remedies involve simple acts like feeding birds or donating specific items.
Immediate Impact: Designed for quick results in stressful situations.
User-Friendly: Written in a way that is accessible to non-experts.
If you are looking for this specific document, I can help you locate the official source or summarize specific remedies for your zodiac sign. Learn about the Pitra Rin (Ancestral Debt) calculations?
Understand the difference between Lal Kitab and standard Vedic astrology? AI responses may include mistakes. Learn more
Unlocking the Secrets of Lal Kitab Amrit Vashist Jyotish: A Comprehensive Guide
Lal Kitab, also known as the "Red Book," is a renowned astrology book written by Pandit Roop Chand Shastri in the 19th century. The book is considered a treasure trove of astrological knowledge, offering insights into various aspects of life, including relationships, career, and spirituality. One of the most sought-after versions of Lal Kitab is the Amrit Vashist Jyotish, which is now available in PDF format. In this blog post, we'll delve into the world of Lal Kitab Amrit Vashist Jyotish and explore its significance, contents, and benefits.
What is Lal Kitab Amrit Vashist Jyotish?
Lal Kitab Amrit Vashist Jyotish is an updated version of the original Lal Kitab, written by Pt. Amrit Vashist. This comprehensive guide to astrology provides in-depth knowledge of various astrological concepts, including planetary positions, zodiac signs, and their influences on human life. The book is a culmination of years of research and expertise in the field of astrology.
Key Features of Lal Kitab Amrit Vashist Jyotish
The Lal Kitab Amrit Vashist Jyotish PDF offers a wealth of information on various astrological topics, including:
Benefits of Lal Kitab Amrit Vashist Jyotish
The Lal Kitab Amrit Vashist Jyotish PDF offers numerous benefits to astrology enthusiasts, including:
Where to Download Lal Kitab Amrit Vashist Jyotish PDF How to Use the PDF for Maximum Benefit:
If you're interested in downloading the Lal Kitab Amrit Vashist Jyotish PDF, you can try searching online platforms, such as:
Conclusion
Lal Kitab Amrit Vashist Jyotish is a valuable resource for anyone interested in astrology. The book offers a comprehensive understanding of astrological concepts, practical remedies, and solutions to common problems. With its updated version available in PDF format, readers can now access this treasure trove of knowledge from anywhere in the world. Whether you're a beginner or an experienced astrologer, Lal Kitab Amrit Vashist Jyotish is an essential guide to unlocking the secrets of the universe.
Disclaimer: Please note that the availability and download links for the Lal Kitab Amrit Vashist Jyotish PDF may vary depending on the source and copyright laws. Always ensure that you're downloading from a legitimate source to avoid any copyright infringement.
Unlocking Your Destiny: The Power of Lal Kitab Amrit Vashist Jyotish (Updated)
Are you feeling stuck in your career, facing hurdles in your personal life, or simply seeking clarity about your future? The updated Lal Kitab Amrit Vashist Jyotish might be the roadmap you’ve been looking for. Created by renowned astrologer Gurudev G.D. Vashist
, this system blends ancient wisdom with modern precision to provide a personalized guide for your life What is Lal Kitab Amrit Vashist Jyotish?
Unlike traditional astrology, this system is a specialized "file-based" Kundli. It combines the unique principles of the original (a 19th-century text on palmistry and astrology) with Vashist Jyotish
calculations. It doesn't just show you a chart; it decodes your birth details into actionable insights tailored to your specific life path. Key Features of the Updated Version: Personalized Analysis
: Detailed insights based on your exact date, time, and place of birth. Life Category Predictions
: Specific guidance on health, wealth, marriage, education, and career. Effective Remedies (Upay)
: Practical, inexpensive solutions to mitigate the negative effects of planets like Rahu or a weak Sun. Long-term Roadmap : Available in variants covering periods from 1 year to 25 years , allowing you to plan your future with confidence. Why It Stands Out Hindi Lal Kitab Amrit Vashist Jyotish - IndiaMART
Truth: Absolutely not. Lal Kitab Amrit strictly prohibits black magic. All remedies are ethical, food-based, charity-based, or nature-based.
Open the PDF and read the “Negative Effects” section. For example, if Saturn is in your 3rd house, the PDF will say: “Courageless, conflicts with siblings, leg pain.” If this matches your life, note it.
Official E-book Stores: Purchase the genuine "Lal Kitab Amrit" by Vashist Jyotish as an official PDF/e-book from platforms like:
Hard Copy + Scanner: Many serious practitioners buy the paperback (approx $10-$15) and create their own personal digital backup for study.
Authorized Astrology Portals: Websites like astrosage.com or clickastro.com often provide excerpts or full chapter downloads of Vashist’s work with permission.
Orion 0.9 is available !
More than one year since the last release already: time flies ! I have been very busy these last months, without a lot of time to dedicate to Orion, but the next version is here (it is actually ready since... last November !), with:
Support for SMP
I'm really happy to announce the support for SMP : Orion will now be able to think using several CPUs/threads in parallel, hopefully resulting in a stronger play ;-)
This required a lot, what am I saying, a ** LOT ** of work: I had to redesign the main parts of the engine, to ensure thread-safe execution, split, refactor, simplify, rearrange the code to avoid problems when computing in parallel. On the contrary, I was surprised by the simplicity of the Lazy SMP approach, that's brilliant !
Smaller network architecture
The other big change is the architecture of the neural network: it is now much simpler than the previous one, for a more or less equivalent strength (~20-30 elo weaker in my own tests). I replaced the 40960x2 weights in the first layer by a simple 768x2 scheme (6 types of piece x 2 colors x 64 squares = 768).
This probably hampers accuracy in some complex positions, but globally speeds up evaluation as you don't have to recompute all the first layer when the king is moving (this is really helpful in endgame positions where kings have more mobility). This choice resulted in a 24x smaller network (421 kB vs 10 MB)...!
I'm really happy with the result: It seems possible to compress chess knowledge a lot !
Important note about originalityI know that some people are looking for originality: do not forget that engine creation can remain for some of us (including me) a hobby and/or a way to learn programming and A.I. !
This has always been my goal: develop a 100% original engine, not only in terms of playing style (that's not the case for the moment) but also in terms of code: Orion is not derived from any other engine, I wrote 100% of the lines of code, in my own way, always after having taken the time to understand what I was doing (the most recent example being the NNUE experiments I led in 2020).
For example, Orion is based on a "only-legal" move generator, using flags embedded in each move representation to help sorting and pruning moves during search. Its transposition table also uses the number of pieces on the board as a criterium to replace old-entries.
But then comes the issue of the data used to train the neural network with the NNUE approach.
As for the 0.8 version, the provided network has been trained on positions that were statically evaluated with the nn-82215d0fd0df.nnue network which is the one embedded in StockFish 12. The StockFish engine itself was not used at all in that process: I took the network, reused the code I developed for my NNUE experiments to read the weights and evaluate a bunch of positions that I collected from CCRL games, and then trained my own network with my own (shared) Cerebrum library (note: this time, I was able to use only 128 million of positions, compared to the 360 million used for 0.8).Finally, from this perspective, I think one cannot consider that Orion is - at this stage - a 100% original work, as it uses knowledge coming from another engine. Please note that starting from v0.4, it has actually been the case: I was previously using StockFish 8 (static) evaluations to tune parameters of my handcrafted evaluation function.
But, for sure, this remains the goal, and I already started to work on reaching that objective...
The "zero" approachI think the most exciting challenge now that I know how to design and train neural networks is to find a way to train a network from zero, i.e. only using results of games (win / draw / loss). Inspired by an idea proposed by Connor McMonigle (Seer author), I tried to train one of such network, without success so far.
The idea is to consider endgame positions (3-4-5-6 pieces), use the results provided by Syzygy tablebases, train a network on these positions, use the engine to evaluate 7-pieces positions with the trained network (after a depth 'd' search), re-train a new network on these labelled 3-to-7 pieces positions, and then restart all the process for 8 up to 32 pieces positions. The beauty of this approach is that the network is trained only using the endgame outcome, and shall learn how to "retropropagate" to middlegame positions the expected result.
Next stepsThis is my current effort: try to improve the way to train such a "from zero" neural network, only relying on game results. That's a very difficult challenge ! Be patient ;-)
Orion 0.8 is released !
I finally managed to build my own "neural network trainer", after a lot of experiments (see here) ! I'm now pleased to release a new version of my little engine Orion, where all the evaluation part relies (only) on a neural network !
Architecture
The architecture of the network used is "NNUE-like", but smaller and simpler than the one used by Stockfish 12 : I was very curious to see to which extent it was possible to "compress" chess knowledge without sacrifying too much strength.
After having tested several combinations, I finally found that halving (*) the first NNUE layer was a good compromise between the loss in strength and the gain in speed (which compensates).
Another change is that all dot products are performed on float values, which is a handicap in terms of speed but simpler from a training perpective. Values of the first layer are rounded and stored as 16-bit integers, resulting in a final 10 MB file.
Training data
Training was performed using 360 million unique positions, extracted from CCRL games, against the nn-82215d0fd0df.nnue network. This network has been released back in August in the public domain by Sergio Vieri, and is now embedded in Stockfish 12.
After 150 iterations ("epochs"), my own tests showed an increase of ~200 elo against v0.7, but this has yet to be confirmed (it is probably highly biased by the fact that I kept the same set of opponents).
The Cerebrum
To help other programmers to understand how to train and use neural networks, I decided to share my work through the "Cerebrum" library, composed of a trainer (Python script) and the corresponding inference code to be embedded in an engine (C langage). The trainer is a cleaned version of the one used for Orion, while the inference code is actually the one used in the engine. I hope all of this will be useful.
What's next ?
This version represents a lot of work. Understanding how neural networks work and how to train them was very challenging ! Now, the next challenge will be to "cut the link" with Stockfish's evaluation. The road is still long but, as we said in French, "Paris ne s'est pas faite en un jour" !
Credits
Credits and a big thank to Sergio Vieri for his incredible work, but also to Yu Nasu for the NNUE concept introduction, and following authors/creators who have worked on its implementation in Shogi and Stockfish (see list here). Last but not least, thanks to the CCRL team for providing games of their tournaments in such a simple way !
Final note
Syzygy support has been removed from this version.
(*) The network architecture is : 2x[40960x128 + 128] x [256x32 + 32] x [32x32 + 32] x [32x1 + 1], where "[W + B]" are the weights (W) and the biases (B).
Orion 0.8 is almost ready !
Next version should be released in a few days, if all goes as expected. In the meanwhile, here is how v0.7 performed in CCRL and CEGT lists : this corresponds more or less to a 110-130 elo increase from the v0.6 : I'm very happy !
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/15 | 114 | 2761 | +25 | -25 | 516 |
| CCRL | 40/2 | 128 | 2736 | +17 | -17 | 1231 |
| CEGT | 40/4 | 121 | 2595 | +15 | -15 | 1350 |
(*) Time control (40/15 means 40 moves in 15 minutes)
Experiments with Neural Networks
I really don't have a lot of time these days, but due to the NNUE on-going 'revolution', and because I'm deeply convinced that this kind of approach is the future, I decided to play a bit with neural networks.
My experiments are related here. To date, I managed to get my own NNUE implementation, giving a serious boost in terms of elo performance (note that this version is purely experimental and shouldn't be considered as the official "Orion" : it is provided only for entertainement/experiments).
I'm currently trying to build a 'neural network trainer' to train my own networks, with the aim to build in a first attempt simpler networks than Stockfish's ones, and test if they can improve current v0.7 evaluation function.
Stay tuned !
Orion 0.7 is available !
Next weeks will be busy, and I won't have a lot of time to work on the SMP version. I prefer to release the new version now, which already includes a lot of rework. Main changes are described in the previous post. I forgot to mention that now Orion also embeds an handcrafted KPK bitbase... and a refreshed logo ;-) As regards Transposition Table (TT) ageing, I opted for a simple implementation : at the beginning of a search, TT is informed of how many pieces remain on board. Every TT entry which is already stored with a greater 'popcount' can be safely and unconditionnaly replaced. This seems sound, and gave good results during my own tests.
I hope the new version will reach a +100 elo increase (when using Syzygy tablebases), but that remains to be confirmed !
Happy birthday Orion ! Last version is just one year old, and performed relatively well with an increase of around 100-110 elo from the previous version : I'm very happy :-)
Next version is on good shape : I managed to achieve some good results, mainly thanks to the addition of Syzygy tablebases support. Some parts of the code have been totally rewritten, like evaluation, magic numbers generation, magic/BMI attacks computation, or static exchange evaluation (again !). Among various changes, aspiration window is finally working, Transposition Table is being "aged" (it was not the case until now, but I choose a different approach than other engines - more details to come), hash move is always tried in Quiescence (even if it's a quiet move which should have not been generated) and before move generation (speed gain), and, finally, PVS is also implemented in Quiescence (surprisingly, this does not seem to be common : maybe I'm doing something wrong). For evaluation tuning, I switched from genetic algorithms to pure linear regression (using Python scripts and Scikit-learn). Orion's evaluation has always been and is still... basic :-) At the moment, gain is between 50 (without Syzygy) and 100 (with) elo. I'm wondering whether to release or not the current development version, but at this stage, I would like to try to implement an important feature which is still missing : multi-CPU support (SMP) !
Current version strength (v0.6) :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/15 | 144 | 2635 | +20 | -20 | 819 |
| CCRL | 40/2 | 149 | 2624 | +17 | -17 | 1204 |
| CEGT | 40/4 | 141 | 2464 | +9 | -9 | 3200 |
(*) Time control (40/15 means 40 moves in 15 minutes)
Orion v0.6 is here !
Almost one year of work... :-) Main changes are :
In my testing conditions (5000 games, 4000 played at 40/1 + 1000 played at 40/2), this version should be ~100 elo stronger than v0.5.
En route for v0.7 and a new long-term objective: reach one day 2800 elo ?!
Orion v0.6 is almost ready to be released !
I'm currently running last tournaments to ensure non-regression with the very last build. It has been almost one year since the last post on this "blog": I worked hard on the new version, continuously trying to improve the engine, test after test... Sometimes, I wonder how other programmers do to improve so quickly their own engine, especially for 2500+ elo engines !
As a rule, and from the very beginning, I always refuse to watch other engines' evalution code. I only took inspiration in code related to search, but only for the parts I can understand and implement on my own. For example, aspiration window at root node is a concept that still doesn't work in Orion. I think I understood the idea, but something is still wrong. As far as evaluation is concerned, Orion's code is 100% original : I only took inspiration from well-known sites like CPW, blogs from other authors (a thought for Mediocre which seems to reborn !) and, for sure, forums (TalkChess being the one I read the more).
Most of the Orion v0.6 progress will come from the evaluation function: I added some concepts and the magic of genetics did the rest :-) In the meanwhile, I'm really proud of the v0.5 strength. This version has been a solid ground to build another release ! After almost one year from its release, here are its current rating:
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 175 | 2529 | +15 | -15 | 1548 |
| CCRL | 40/4 | 176 | 2513 | +13 | -13 | 2348 |
| CEGT | 40/4 | 166 | 2352 | +13 | -13 | 1922 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.5 is available !
After several weeks of hard work, and a huge number of games played to test, test, and test again, I'm pleased to release a new version of my little engine Orion !
So, what's new ? Not a long list of new features, but a lot of code changes and rewrite :
Yes ! It worked ! I finally managed to improve strength using PBIL method (a big thank to Thomas Petzke) ! v0.5 is the first genetically modified version of Orion :-)
Gain can appear low, but I use a simple and straightforward fitness evaluation method : only compare score difference between Orion and Stockfish (v8). My previous attempts failed because of a bad initialisation of weights. Tuning only applied on evaluation terms, from 25 millions of unique positions extracted from CCRL 40/40 games and took ~ 8-10 hours. For the next release, I'll try to include search parameters but this will need to change fitness evaluation and run games : it should really take a lot of time !
Lastly, I tried to improve my testing framework. In previous versions, I only ran gauntlets against my 3 prefered partners : iCE, Lozza, and Madchess. I now run 4000 games against 20 engines, at 40 moves / 60 seconds, using the Hert500.pgn opening book. To preserve my computer, CPU is underclocked at 2.24 GHz. A complete run takes ~ 36 hours (7 games are run in parallel).
I hope all this work will be reflected in an elo gain in real conditions !
Orion v0.5 is approaching !
Since the release of v0.4, I have been working a lot to try to improve Orion, testing dozens of code changes and playing thousands of games. I finally started to get promising results a few weeks ago. I'm currently trying to grab a few more elos before releasing a new version.
In the meanwhile, I'm really satisfied to see that current version performs relatively well in tournaments (it is 60-100 elo stronger than v0.3 !). Compared to previous versions, v0.4 is clearly a strong and sound basis to try new ideas. You'll find in the table below an idea of its current strength. I'm really excited with the current development version : stay tuned !
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 188 | 2447 | +24 | -24 | 581 |
| CCRL | 40/4 | 205 | 2420 | +18 | -19 | 1045 |
| CEGT | 40/4 | 185 | 2262 | +14 | -14 | 1550 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.4 is out !
I'm really happy to release this new version : I worked a lot on it, testing tens of versions, to finally get a version doing what it was intended to :-)
From the source code perspective, this version does not vary a lot from the previous : I only made small adjustements on search and fixed some pieces of code that didn't do what I expected to.
Evaluation was just modified to adjust rooks scoring. I gave a new chance to PBIL algorithm to improve it with no results. This time, I tried to minimize the difference between Orion and Stockfish v8 scores, but in real games, it didn't give better play.
So, what's new in this version ? Even if final code differences are small, there are some big changes:
In addition, a BMI2 version of the engine is now provided, giving a small speed bonus (+ 5%) on compatible systems.
Why releasing a new version now ? Because, even if evaluation has not been improved, my own tests show a clear progression against v0.3 : +/- 100 elo at 40/4 ! I hope this will be confirmed in real tournaments and longer time controls...
Have fun and do not hesitate to give me feedback !
It has been nearly a year since Orion was put online, and you will find in the table below a good idea of its level. I'm quite happy with these results (many thanks to all testers) ! In fact, the engine performed better than I expected. However, during last months, I tried to improve again the last version but faced difficulties... Developing a chess engine can really cause headaches !
I first tried to improve my evaluation function (using genetic algorithms) : it only allowed me to validate my PBIL framework as real strength was finally not increased...! After multiple attempts, I suspected pruning and reductions techniques had (bad) influence while trying to optimize evaluation.
I then started to inspect search tree implementation to decide what to deactivate, and found some bugs and pieces of code not doing what they ware intended to... Several hundreds games later, I also suspected problems in Transposition Table, notably on replacement strategies. I then tried multiple approaches... before being satisfied.
I'm here. And last results seem to go in the right direction, but it's too early to release a new version : a lot of work is still planned ! I want first to stabilize search tree implementation and then give a new chance to genetic algorithms to improve Orion's evaluation function. For the latter, I think I will disable pruning and reduction techniques to better converge to a good solution...
During all my efforts, I also found time to implement a BMI2 version of the engine, giving (on compatible systems) an incredible... +0% speed boost ! Another disappointment... and a new source of forthcoming debug sessions :-)
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 203 | 2383 | +22 | -22 | 686 |
| CCRL | 40/4 | 209 | 2345 | +18 | -18 | 1187 |
| CEGT | 40/4 | 204 | 2162 | +13 | -13 | 1600 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.3 is now available !
I'm very happy to release this new version after several weeks of hard work. It (almost) consists in a complete rewrite of the previous version, in order to have a more readable and robust code, which should be a better basis for further enhancements. And code is not throwing anymore tons of warnings when compiling ;-)
Aside from rewriting, some features have been added, changed or removed :
Evaluation is unchanged. The new pruning techniques allow smaller search trees while adding some search instability. It results in less reliable moves in shallow depths, but should increase strength for longer time controls. I'm very impatient to see how it will behave in tournaments !
Next version will focus on evaluation enhancement with a PBIL framework already implemented and ready to be played with !
New Orion v0.2 ratings :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 203 | 2266 | +38 | -38 | 230 |
| CEGT | 40/4 | 1346 | 2105 | +25 | -25 | 600 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.2 participated in its first tournament ("Special Stars", organized by CCRL team) and finished in 4th place !
As it was my goal to compete with other engines, I'm very proud of it ;-)
First feedback from testers with computers that don't support 'popcnt' instruction show that the engine may crash : this problem has been fixed and a patched version of Orion v0.2 has been repackaged in the zip file (see download section).
This shows that we never test enough ! Thanks to all testers for their patience...
Please report any new problem here.
I'm pleased to announce the release of Orion v0.2 !
This new version includes :
All these features should improve the engine speed :-)
Please enjoy !
The CEGT team tested intensively Orion v0.1... playing 1100 games ! Here is the rating obtained :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CEGT | 40/4 | 1360 | 2048 | +18 | -18 | 1100 |
(*) Time control (40/4 means 40 moves in 4 minutes)
After the last CCRL update (many thanks to all testers !), these are the ratings of Orion v0.1 :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/4 | 229 | 2167 | +39 | -39 | 248 |
| CCRL | 40/40 | 220 | 2194 | +116 | -108 | 30 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.1 is now listed in CCRL (in the "complete list" only, because It played less than 200 games) !
After 30 games played, Orion has been evaluated at 2194 elo. The error margin is quite big (+/- 116), but totally normal since only a few games were played. I think its real level is closer to 2078 :-)
I'm very happy and proud to release the first version of my UCI chess engine : Orion v0.1 !
I started to work on it several years ago, as a hobby, but decided to rewrite it entirely (and more seriously) at the beginning of the year, switching from Java (easy for prototyping) to C (easiest to distribute).
It includes :
My long term goal is to reach 2500 elo (one day ?!), but for the moment, this version seems to have, let's say, some room for improvement :-)
It's an 100% original work (no fork/derivative), a lot inspired by chessprogramming.wikispaces.com, and ideas taken from the excellent blogs of Jonatan Pettersson (Mediocre) and Thomas Petzke (iCE).
In order to use Orion, you will need a GUI like Arena.
Last but not least, many thanks to Graham for accepting Orion to enter the CCRL competition !
Please enjoy !
Orion is free : you can download and use/test it without limitation/restriction. The zip contains a Windows executable, a personal logo (astronomy is another passion), and a network file. You are allowed to redistribute it or its elements, on the absolute condition that you don't modify them. Sources of the engine are not included since development is in a too early stage. From v0.8, a part of Orion has been released under the MIT license ("The Cerebrum" library).
Download v1.1 (64-bit version)
Previous versions : Download v0.1 to v1.0 (64-bit versions)