Skip to main content

RL Chess Engine (Self-Play + Human Mimic)

Overview: A 180-day curriculum to build two chess AIs: one learned purely via reinforcement learning self-play (inspired by AlphaZero), and one that mimics human play style (trained on human game datasets, akin to Maia chess). The program starts with constructing a basic chess environment and a policy-gradient agent, then progresses to implementing Monte Carlo Tree Search (MCTS) and self-play training to create a strong engine. In parallel, it covers supervised learning on millions of human games to create a human-like engine. We emphasize training pipelines (self-play game generation, distributed training), evaluation (Elo, playing vs Stockfish), and adjustable difficulty settings. The final deliverables are an RL-based chess engine that can play at various skill levels and a “mimic” engine that plays like a specific human or rating level.

  • Tech Stack: Python chess libraries (e.g., python-chess for game rules and move generation), OpenSpiel or custom environment for self-play. Neural networks in PyTorch for policy and value functions. MCTS implementation in Python (with C++ optimization for speed if needed). Distributed training tools (to run self-play games in parallel processes). Datasets: Lichess open game database (billions of games) for supervised learning; FICS or other PGN databases for specific player data. Logging with TensorBoard (track Elo estimates, loss curves). A GUI like Lichess or Arena for playing against the engine (using UCI protocol).
  • Key Algorithms & References: Policy Gradient (REINFORCE) basics for initial agent. AlphaZero algorithm (Silver et al. 2018) – self-play with neural network and MCTS. MCTS algorithm (UCT search, playouts) integrated with network’s policy prior and value estimate. Elo rating system for evaluation. Maia Chess project (Microsoft Research) as reference for human-like training – Maia was trained on millions of Lichess moves and matches human moves >50%. Papers: “Mastering Chess and Shogi by Self-Play...”, and Maia Chess paper/blog.
  • Project Repos & Tools: Leela Chess Zero (LC0) project (open-source implementation of AlphaZero for chess) for inspiration and possibly reuse of network architecture or training tricks. Maia Chess GitHub for human-trained models at different Elo. OpenSpiel’s chess environment or ChessEnv (if available) for standardized interface. Stockfish engine (for sparring and evaluating strength).

Block 1 (Days 1–15): Environment Setup & Baseline Agent​

  • Day 1-5: Set up a chess programming environment. Install python-chess library which provides board representation and move generation. Verify that you can programmatically make moves and detect game end (checkmate/stalemate). Write a simple loop to play random moves between two players using python-chess to ensure the environment is correct. Establish a code structure: a Game class, a NeuralNetwork class (initially might be a stub), and a Trainer script. Deliverable: Basic chess environment running a random-vs-random game to completion (logged moves, result).
  • Day 6-10: Implement a simple policy-gradient chess agent. Start with a very weak baseline: for example, a policy network that looks at the board and chooses a move (initially this network can be very small or even random for testing the pipeline). Because reward (win/loss) is extremely sparse (comes after potentially 100+ moves), implement a simpler reward for testing: give a small reward for capturing a piece and a negative for losing a piece, just to have some signal (not a true goal but useful to test learning). Train two identical agents self-playing each other using REINFORCE: after each game, reward = ±1 for win/loss plus intermediate piece rewards. Update the policy parameters accordingly.
  • Day 11-15: Evaluate if the policy improved above random (it might learn to favor capturing pieces due to shaped reward). This is not yet a strong or valid chess strategy, but it tests the training loop. Remove or reduce the shaped rewards gradually, moving toward the true goal (winning) only. You may see that learning becomes much harder – discuss the need for advanced techniques (like value networks, MCTS) for full-game credit assignment. Deliverable: A rudimentary RL agent that plays some recognizable chess (doesn’t hang pieces intentionally, for instance). Also, a clear analysis of why pure policy gradient on chess is challenging (huge state space, sparse reward), motivating the next steps.

Block 2 (Days 16–30): Integrating Monte Carlo Tree Search (MCTS)​

  • Day 16-20: Implement the Monte Carlo Tree Search algorithm for decision-making. Use a simplified version first: random rollouts to end of game to evaluate moves (which will be extremely weak but proves the concept). Connect MCTS to the environment: from a given board, MCTS will simulate many random games for each possible move to estimate win rates. Test MCTS by itself: does it prefer reasonable moves over completely random play? (Likely it will marginally prefer moves that avoid immediate losing, but with random playouts it’s still weak.)
  • Day 21-25: Upgrade MCTS using the neural network. Design a neural network architecture for the chess state: input could be an 8x8xN tensor (N channels encoding pieces and maybe history), similar to AlphaZero’s architecture. The network will output: (a) a policy vector (probability for each move) and (b) a value (estimated win probability from that state). At this stage, initialize the network with random weights or supervised learning (if you have any preliminary data). Integrate the network into MCTS: now each leaf node uses the value net instead of random rollout, and MCTS uses the policy net’s suggestion as prior for which moves to explore more (i.e., PUCT formula as in AlphaZero).
  • Day 26-30: Test the integrated MCTS+NN on a few positions. Initially, with a random network, it will effectively behave randomly. So optionally, initialize the network by supervised learning on a small set of human games or on self-play games of a simpler baseline (like train it to predict moves of a weak engine or some opening moves). Even without that, proceed to self-play training: have the agent play many games against itself using MCTS for move selection (this generates games that are stronger than purely random because MCTS, even with random net, adds some search depth). Store these games. Train the neural net on these games: specifically, for each position in the self-play games, train the policy head towards the MCTS move probabilities and the value head towards the game outcome (win=1, draw=0, loss=-1). This is the AlphaZero-style training loop: self-play to generate data, then train network, which in turn guides the next self-play iteration. Deliverable: A functioning self-play training loop with MCTS. After a few iterations (even if small), the network should start to encode some knowledge (e.g., it might learn basic mates or piece value approximations). Although strength is low at this point, the framework for building a stronger engine is in place.

Block 3 (Days 31–45): Self-Play Reinforcement Learning (AlphaZero Method)​

  • Day 31-35: Scale up the self-play. Increase the number of self-play games generated per iteration (e.g., 1000 games per iteration). Implement parallelism: run multiple self-play games in parallel processes or threads to collect data faster. Use MCTS with a reasonable number of simulations per move (say 800 simulations as in AlphaZero, if computationally feasible). Incorporate exploration in self-play: AlphaZero uses Dirichlet noise on the prior at root to ensure a variety of moves are explored. Add this so the self-play games don’t all follow the same path.
  • Day 36-40: After a few iterations, start evaluating the current net’s strength. Pit the latest network against older versions (from previous iterations) to see improvement (the typical approach is to have an Elo rating or at least a win-rate comparison). Also, play against a known baseline: for example, have it play against Stockfish set to a low level or a simple engine like Sunfish (a naive minimax engine). Initially, it might lose, but track progress (perhaps it goes from losing 100% to winning a few games as it improves).
  • Day 41-45: Focus on the neural network improvements. Perhaps increase the network size (more layers or filters) as more data becomes available. Regularize to avoid overfitting to self-play data (though self-play continuously generates fresh, on-policy data). Consider adjusting training hyperparameters: learning rate schedule, using momentum or Adam optimizer settings as recommended by AlphaZero paper. Deliverable: A progressively improving chess engine. For example, by Day 45 it might reliably beat random play and perhaps challenge a very weak conventional engine. You should have a curve of self-play Elo or win-rate that shows the agent is learning over iterations (mirroring how AlphaZero went from random to superhuman in hours, though our resource-constrained version will be much slower/weaker).

Block 4 (Days 46–60): Strengthening the RL Engine​

  • Day 46-50: Extend the training time and add more knowledge. If possible, incorporate more sophisticated search enhancements: e.g., opening book generation from self-play (the engine can store moves it found good in early game) or endgame tablebase for very late-stage (if for simplicity, maybe code in knowledge of trivial endgames like king + queen vs king so it doesn’t stumble there). These are optional, as we aim for a pure RL approach, but noting them is good for completeness.
  • Day 51-55: Introduce multi-condition training to achieve configurable skill levels. One idea: during self-play, occasionally handicap one side (like give one side fewer simulations or a weaker network) to produce games of various skill levels. Label the self-play games with a “skill level” context (maybe how many MCTS sims were used). Train a single network that can adjust strength via an input parameter (this could be an additional feature plane or input to the network indicating desired skill). This is advanced, but lays groundwork for one network to play at different levels. Alternatively, simply plan to save snapshots of different training stages as different skill levels (earlier iterations are weaker, later are stronger).
  • Day 56-60: Emphasize evaluation and validation. By now, the RL engine might be decent (maybe at least as good as an amateur human, depending on compute). Have it play a structured match against Stockfish on progressively higher difficulty settings until it consistently loses – note its approximate Elo. Also have it play some games against human moves (maybe you or other testers play against it via a GUI) to get a sense of style – typically, pure RL self-play agents have non-human, sometimes alien strategies. Save a few impressive games played by the engine (for example, if it discovers a classic tactic or an unusual gambit that works, include that). Deliverable: A mid-project report on the RL engine’s status: including its estimated rating, strengths/weaknesses (maybe it is tactically strong but positionally odd). Also, the codebase now should have a command-line interface where one can play the engine via UCI (Universal Chess Interface) or a simple text input.

Block 5 (Days 61–75): Human Data Collection & Processing​

  • Day 61-65: Start the human mimicry track. Obtain a large set of human chess games. Use the Lichess open database which has millions of games per month available in PGN. Filter this data if needed: for example, focus on games in a certain rating range or games of a particular player if mimicking a specific individual. If the goal is a specific person’s style, gather as many of their games as possible (say, all games of “Player X”). If the goal is a general human-like style, collect a broad set (e.g., all games of club-level players 1500–1800 Elo).
  • Day 66-70: Parse the PGNs into training data for a neural network. Decide on network architecture – likely similar to the RL policy network. For each position in the game, the input is the board state, and the target output is the move made by the human. This is a supervised learning classification problem over the move space. One challenge: the move space is large (all legal moves ~ up to 4672 in worst case, but realistically much fewer). Represent moves as indices (e.g., use a convention to index all possible moves; python-chess can map moves to UCI string, which you can hash to an index). Build a dataset of (state -> move index). You might restrict to top N most common moves to make output dimension manageable, or output a mask of legal moves and apply softmax only on those during training.
  • Day 71-75: Begin training the human-like policy network on this dataset. Use a large batch size and perhaps an Adam optimizer. Monitor the accuracy: e.g., “move prediction accuracy” on a validation set. Top-1 accuracy might be low (chess has many reasonable moves), so also monitor top-3 or top-5 accuracy. If mimicking a specific player, accuracy will reflect how deterministic that player is. Overfitting is a concern if dataset is small (not likely if thousands of games). Implement early stopping or regularization. Deliverable: A trained “Maia-like” model that, given a board position, predicts what move a human (or the specific player) would make. Expectation: it should pick strong but sometimes imperfect moves, matching typical human errors at that level. For instance, it might not always find the absolute best engine move, especially if it never saw it in human games.

Block 6 (Days 76–90): Mimic Model Integration and Analysis​

  • Day 76-80: Evaluate the human-like model. One way is to use it as an engine: have it play against the RL engine or a known engine. Because it’s trained to mimic, not to win, it might make suboptimal moves reflecting human style. We need to see this in action: perhaps play a match between the mimic model (sampling from its move probability distribution) and Stockfish at a low level. It will likely lose, but observe *how… (continuing) …
  • Day 81-85: Analyze the mimic agent’s style. Compare statistics from its games to the target human’s games: opening move preferences, aggressiveness (e.g., frequency of sacrifices or blunders). If mimicking a specific player, see if the AI reproduces known tendencies (does it favor certain openings that player uses?). You can also measure move match rate: in positions from the player’s real games, how often does the mimic AI choose the exact move the human did? (Maia reported over 50% move match for its target rating level.) If the match rate or style alignment is unsatisfactory, fine-tune the model with more data or adjust the network (perhaps include a few moves of context, not just the current board, to capture the human’s move patterns).
  • Day 86-90: (Optional) Introduce a blunder model to the mimic agent for realism. Real players err occasionally, so you might deliberately sample a slightly suboptimal move from the mimic policy to simulate this. For example, set a temperature > 1 on the output distribution so it sometimes picks lower-probability moves (which are often mistakes) rather than always the top choice. This can make the mimic’s play more human-like (not perfect), especially if the training data was mostly strong moves. Deliverable: A finalized “human-like” chess engine that plays at the desired skill level or in the style of the specific person. This model will be used independently as the mimic agent in the final deliverables.

Block 7 (Days 91–105): Configurable Skill Levels for the RL Engine​

  • Day 91-95: Develop a strategy for adjustable difficulty in the RL engine. Easiest approach: maintain multiple checkpoints of the self-play trained model at various training stages (earlier versions are weaker). Alternatively, use one strong model and handicap it during play: e.g., limit MCTS simulations or introduce randomness. Implement a mechanism to limit the engine: for “Level 1” difficulty, allow only, say, 100 MCTS simulations per move and add a high exploration noise so it plays more casually. For higher levels, increase simulations (making it play stronger, as more search = better play) and reduce noise. Document how each level corresponds to approximate strength (you might estimate Level 1 ~ beginner, Level 5 ~ club player, Level 10 ~ expert, etc., based on testing).
  • Day 96-100: Test each skill level against known benchmarks. For example, use Stockfish set to various Elo ratings or use the Lichess AI levels (which are calibrated) as opponents. Record win/draw/loss for, say, 20 games for each level of your engine against a reference opponent. Adjust the configuration if needed to better align (if your Level 3 engine is too strong and beats a 1500 Elo bot consistently, you might add more noise or reduce search to tone it down).
  • Day 101-105: Finalize the skill settings and implement an easy way to choose them (for instance, a command-line flag or function parameter “engine.set_level(n)”). Ensure that even at the lowest level, the engine doesn’t make illegal moves or completely random moves – it should still follow basic principles, just more prone to mistakes or shallow tactics. At the highest level, it should use the full power of the trained network and MCTS (which by now might be playing at an advanced level, possibly International Master strength if sufficient training was done, though likely lower given resource limits). Deliverable: A single RL engine that can play at multiple strengths. A table mapping engine level to estimated Elo based on your testing (e.g., Level 1 ~ 800 Elo, Level 5 ~ 1500 Elo, Level 10 ~ 2000 Elo as an example) and evidence supporting those estimates.

Block 8 (Days 106–120): Engine Evaluation and Tuning​

  • Day 106-110: Conduct a thorough evaluation of the strongest version of the RL engine. Play a 100-game match vs Stockfish at a fixed setting (like Stockfish on difficulty level 4 or 5). Calculate the win/draw/loss and an Elo rating using the Elo formula. If possible, use arating tool or an online platform’s evaluation. Also have the RL engine play the mimic engine in a match – the RL engine should typically win if it’s much stronger, but observe if the mimic engine finds moves the RL hasn’t seen (since RL was trained on self-play, it might be unfamiliar with some human lines). This can reveal any blind spots of the RL agent (e.g., it might not know how to respond to certain human opening gambits if they never appeared in self-play).
  • Day 111-115: Fine-tune the RL engine’s training if needed based on evaluation. For instance, if the engine struggles with draws or certain endgames, you could incorporate draw reward (giving a slight reward for stalemate so it doesn’t force unnecessary risks) or train on specific endgame scenarios separately (a mini self-play on endgames). If it has an opening weakness, you could seed some self-play games with common human openings (start games from popular opening positions rather than always from the standard initial position) to broaden its experience. These tweaks can improve its practical play.
  • Day 116-120: Ensure the engine is robust for different time controls. Test it in fast games (blitz) by limiting thinking time, and in longer games by allowing more time/MCTS simulations. Verify stability (no crashes, no illegal move generation at any level). Begin integration with a GUI: implement the Universal Chess Interface (UCI) protocol so that the engine can be loaded into popular chess GUIs (like Arena or Fritz) or played on Lichess as a bot. Deliverable: Evaluation results of the RL engine (e.g., “Engine reached ~1800 Elo in self-play and beat Stockfish level 4 in 60% of games”). The engine should now be a polished product that can be plugged into chess interfaces and configured for different play strengths.

Block 9 (Days 121–135): User Experience and GUI Integration​

  • Day 121-125: Develop a simple Graphical User Interface or utilize an existing one to allow users to play against your engines. If using an existing GUI (like connecting to Lichess via their API for a bot account), set that up for both engines (RL and mimic). Alternatively, create a minimal GUI with pygame or web (perhaps using a chessboard JS library) where a user can make moves and the engine responds. Focus on making it easy to switch engine modes (strong, weak, mimic).
  • Day 126-130: Add features for the user experience: for example, a slider to choose engine skill level, an option to play against the “Mimic [Player X]” engine or the “AlphaZero-style” engine. If targeting a specific person’s style, you could even load a particular opening book of that player’s games to have the engine begin games exactly like that player often does. Implement move delay to make it feel more human (instant moves feel artificial at lower levels, so add a second or two delay as if ‘thinking’, especially for the mimic engine).
  • Day 131-135: Testing the GUI/UX: have a few people play against the engines and gather feedback. Ensure the mimic engine indeed feels like playing a human (maybe a particular known style), and the RL engine at low levels is beatable by casual players while at high level it challenges strong players. Fix any issues (e.g., GUI not updating, or engine not resigning in lost positions – add a resign threshold for realism). Deliverable: A user-friendly interface (or integration with an existing one) where a user can choose to play the AI at various skills or against a human-like persona. This transforms the project from just code into a playable product.

Block 10 (Days 136–150): Final Demonstrations​

  • Day 136-140: Prepare a showcase match: perhaps your RL engine vs a known engine (Stockfish or another open source engine) with commentary. You can pick a level for your engine such that it’s an interesting game. Record the game or play it live, annotating key moments (e.g., “Our RL engine sacrifices a knight for positional advantage – a very AlphaZero-like tactic!”). Also prepare a demonstration of the mimic engine: for instance, compare how the mimic engine and a human (the one being mimicked) each play a given position. If the mimic is of a famous player, show it reproducing a signature move.
  • Day 141-145: Demonstrate the adjustable difficulty: perhaps line up three games in parallel: one where a beginner plays against level-2 engine (and manages to win or draw), one where an intermediate plays level-5 (competitive), and one where no one can beat level-10. This highlights the engine’s range. If feasible, deploy the engines on an online platform and let people play a few games (this could provide real-world feedback and also be a cool final presentation – “come play our AI”).
  • Day 146-150: Create final presentation materials. Slides or a report describing the journey: starting from scratch, implementing self-play and MCTS, improving to a respectable strength, and creating a unique mimic engine that “learns from humans instead of purely against itself.” Include graphs (training progress, move prediction accuracy for mimic, Elo vs iterations). Possibly include a short snippet of code in the appendix to illustrate key components (like the elegant simplicity of the AlphaZero training loop in pseudo-code). Deliverable: A set of curated games and a live demo session. For example, a final event where the RL engine plays the mimic engine at the mimic’s rating level – ideally the RL engine wins but the mimic engine puts up a very human fight, showing off both AI’s capabilities. This serves as a capstone demonstration of all features.

Block 11 (Days 151–165): Documentation and Handoff​

  • Day 151-155: Write comprehensive documentation for both engines. The RL engine docs should cover how to continue training it (in case one wants to push it further), how to adjust settings, and how the MCTS works with the network (perhaps include pseudocode or flowcharts). The mimic engine docs should detail the data used (with acknowledgments to Lichess for their open data), how to retrain for a different player or update with more games, and any limitations (e.g., it won’t generalize to positions vastly different from what it saw).
  • Day 156-160: Package the code and models. Provide the final neural network weights for the strongest engine and the mimic engine. Ensure the repository is organized: maybe one folder for the RL training code, one for the mimic training code, and one for the engines/deployment (with UCI interface). Include a README with quickstart instructions (e.g., “run play_rl_engine.py --level 5 to play against the engine at level 5 on the console”). If possible, also provide the trained models for different difficulty levels (or a script that can load the main model and simulate limited strength).
  • Day 161-165: Final review meeting. Present the complete work to the team or stakeholders. Discuss how the RL engine achieved learning purely through self-play (cite Silver et al. that AlphaZero learned superhuman play tabula rasa, your engine is a scaled-down proof of concept of that). Also emphasize the human-like engine – perhaps mention Maia Chess as validation that similar approaches can closely mirror human play. Address questions, for example about scalability (what if we had more compute – it could reach grandmaster level) or about extending to other games. Deliverable: Project closure with all deliverables handed over – a strong chess engine with multiple difficulty settings and a human-mimicking chess AI, fully documented. Both are ready for integration into chess platforms or further development, fulfilling the objectives of the 6-month training program.

Block 12 (Days 166–180): Post-mortem and Future Work​

  • Day 166-170: (Optional, reflection) Conduct a project post-mortem analysis. What were the key challenges? (e.g., managing the enormous search space of chess, ensuring stability of self-play learning without divergence, etc.) What could be improved with more time or resources? Document these insights. For instance, “In future, one could incorporate MuZero, which learns without knowing rules and could improve efficiency,” or “Our mimic engine could be extended to mimic specific styles at will by conditioning on a style vector, an area for research.”
  • Day 171-175: Suggest how this work could be applied or extended. Perhaps using the mimic engine to train the RL engine (learning from human games first then self-play, which is known to speed up learning) – essentially combining the two approaches. Or deploying the engine in an online tournament to gauge its performance against human players worldwide. This forward looking section isn’t a deliverable per se but adds value to the documentation.
  • Day 176-180: Wrap up any remaining loose ends. Ensure any external libraries or credits are properly cited. Push final code to GitHub if open-sourced. Final Deliverables:
    • AlphaZero-like Chess Engine: An RL-trained model using self-play and MCTS, with a configurable strength setting.
    • Mimic Chess Engine: A model trained on human games, emulating a specific player or play level.
    • Both engines are accessible via UCI for integration into chess GUIs and come with complete documentation and example usage. With that, the RL Chess Engine program is complete, having produced a sophisticated chess AI suite over 6 months.

Resources​