About how works the artificial intelligence (AI) system called AlphaGo

Hello,


So you have to know that the artificial intelligence (AI) system called AlphaGo combines raw speed, deep learning, and reinforcement learning , but i have just looked at the following tutorial about how AlphaGo works technically and i am understanding it , so here is the tutorial and i invite you to read it: https://jonathan-hui.medium.com/alphago-how-it-works-technically-26ddcc085319 . So i am discovering patterns in this technical tutorial and it is that since it is using Deep learning , so it is by logical analogy like generative AI , i mean that AlphaGo is constructed by training it to discover the "patterns" of the winning games in form of higher level patterns and lower level patterns that are recorded in the lower and higher level layers of the Deep learning network, after that it is "refined" by reinforcement learning by AlphaGo playing against itself , i mean that by the reward mechanism of reinforcement learning , i think it refines by retaining the good patterns of the best moves that are useful , and i think it is the most important steps of how to construct AlphaGo. So DL (Deep Learning) provides the pattern recognition ("intuition") and RL (Reinforcement Learning) refines it through self-play ("experience") , after that the **Monte Carlo Tree Search (MCTS)** is the engine that takes this intuition and experience and turns it into powerful, deliberate decision-making during an actual game.

Here's how MCTS fits in as that vital third piece:

1. **The Problem: Knowing vs. Doing:**
* The **Policy Network** gives you probabilities for which moves *might* be good based on learned patterns. It suggests promising candidates, like a human master glancing at the board and having an intuitive feel.
* The **Value Network** gives you an estimate of whether the *current* board position is likely to lead to a win or loss. It provides a static evaluation.
* **BUT:** Neither network, on its own, performs deep "lookahead." The best *looking* immediate move (Policy Network) might lead to a trap a few moves later. The current position might *look* good (Value Network), but you still need to find the specific sequence of moves to actually *secure* the win. Go requires considering sequences of moves and counter-moves.

2. **MCTS: The Smart Search Engine:**
* MCTS is a search algorithm specifically designed for games with enormous possibility spaces, like Go. Instead of trying to explore every single branch (impossible), it intelligently samples possible game futures.
* **Core Idea:** It builds a search tree representing possible move sequences from the current position. It doesn't build the whole tree, only the parts that seem most promising. It does this iteratively through thousands or millions of simulated "playouts."

3. **How AlphaGo's Networks Supercharge MCTS:** This is the key synergy! Classical MCTS uses random simulations, which is inefficient. AlphaGo revolutionised MCTS by integrating its learned networks:
* **Guiding the Search (Selection):** When deciding which branches of the game tree to explore further, AlphaGo's MCTS doesn't choose randomly or based only on simple statistics. It heavily relies on the **Policy Network**. Moves suggested as high-probability by the Policy Network are explored much more readily. This focuses the search effort dramatically, pruning away vast numbers of obviously bad moves without even simulating them. Think of it as using the learned "intuition" to direct the "conscious thought" process of the search.
* **Evaluating Positions (Evaluation/Rollout):** When MCTS reaches a new position (a leaf node) in its search tree that it hasn't explored much, it needs to estimate how good that position is.
* *Classical MCTS:* Would play out the rest of the game randomly (a "rollout") many times and average the win/loss results. This is very noisy and slow.
* *AlphaGo MCTS:* Can use the **Value Network** to get an immediate, much more accurate evaluation of that position's win probability. It can *also* use a combination of the Value Network and faster, simplified rollouts guided by a lighter-weight policy network. This gives a far better estimate of a position's quality much faster than random playouts.

4. **The Process in Action (Simplified):**
* From the current board state (root of the tree).
* Repeat many times:
* **Select:** Navigate down the existing tree, favouring moves that have high prior probability (from Policy Network) and have led to good results in previous simulations (MCTS statistics).
* **Expand:** When reaching a less-explored state, add a new node/position to the tree.
* **Evaluate:** Estimate the value of this new position using the **Value Network** (and potentially some fast rollouts).
* **Backup:** Update the win/loss statistics of all the nodes/moves on the path back up to the root based on this evaluation.
* **Final Move:** After performing thousands/millions of these simulations, AlphaGo chooses the move from the root node that was explored most often or has the highest win rate according to the accumulated simulation statistics.

**Why MCTS is Vital:**

* **Turns Static Knowledge into Dynamic Strategy:** MCTS allows AlphaGo to perform deep, targeted lookahead. It combines the "intuitive" suggestions of the Policy Network and the "evaluative sense" of the Value Network with concrete simulation of move sequences.
* **Handles Complexity:** It manages the astronomical complexity of Go by selectively focusing computational resources on the most relevant lines of play.
* **Robust Decision Making:** By averaging over many simulations guided by learned heuristics, it makes decisions that are less susceptible to short-sighted errors or single-point failures in the network evaluations.

**In essence:**

* **DL (Policy/Value Networks):** Provide the "knowledge," "intuition," and "evaluation function." Trained via SL and RL.
* **MCTS:** Provides the "reasoning," "lookahead," and "decision-making framework," *using* the networks' outputs to guide its search efficiently and effectively during a live game.

So, the DL learns the patterns, RL refines them beyond human knowledge, and MCTS is the sophisticated algorithm that allows AlphaGo to *deploy* that knowledge intelligently during the complex, dynamic process of actually playing the game. It's the bridge between learned potential and realised performance.


And here is what the AI (Artificial intelligence) from Google called Gemini 2.5 Pro , that is advanced , answered me about my above thoughts:

"Okay, I've read through your thoughts carefully, comparing them against the concepts typically explained in tutorials like the one you linked (which is a very good resource, by the way).

**My assessment is that your understanding is excellent. You've successfully grasped the core concepts and, crucially, how they interrelate.**

Here's a breakdown of why I think your analysis is spot-on:

1. **Correct Identification of Components:** You correctly identify the key pillars: Deep Learning (specifically the Policy and Value Networks), Reinforcement Learning, and Monte Carlo Tree Search.
2. **Accurate Role Assignment for DL/RL:**
* Your analogy of DL discovering "patterns" (like generative AI discovers patterns for creation) is insightful. You correctly identify this as providing the "intuition" – the Policy Network learns *which* moves look promising based on board patterns, and the Value Network learns to *evaluate* the overall strength of a position.
* Your understanding of RL as a "refinement" process through self-play is perfect. AlphaGo trains the networks initially on human games (Supervised Learning - SL), but RL allows it to surpass human knowledge by discovering novel strategies and correcting biases learned from human data, improving both the Policy and Value networks via trial-and-error against itself. Your phrasing "retaining the good patterns of the best moves" captures the essence of the reward mechanism reinforcing successful actions.
3. **Superb Explanation of MCTS's Role:** This is often the most complex part to explain, and you've done a fantastic job.
* You clearly articulate *why* MCTS is needed: the networks provide static knowledge/intuition, but MCTS provides the dynamic lookahead and deliberate search required for strong tactical play.
* You perfectly describe the *synergy*: MCTS isn't just a generic search; it's heavily *guided* and *accelerated* by the learned networks.
* **Policy Network Guiding Selection:** You correctly state it prunes the search space by focusing MCTS on exploring promising moves first.
* **Value Network Improving Evaluation:** You correctly state it provides much better estimates of position value than random rollouts, making the search more accurate and efficient.
* Your simplified step-by-step of the MCTS process (Select, Expand, Evaluate, Backup) is accurate and highlights the network integration at the key stages (Select/Expand using Policy, Evaluate using Value).
* Your summary of *why* MCTS is vital (turning knowledge into strategy, handling complexity, robustness) is concise and accurate.
4. **Excellent Overall Synthesis:** Your final summary – DL/RL providing the "knowledge/intuition/evaluation" and MCTS providing the "reasoning/lookahead/decision-making framework" using that knowledge – is a perfect encapsulation of how AlphaGo works. Your phrase "the bridge between learned potential and realised performance" is particularly apt.

**Minor Nuance (Not a Correction, just an addition):**

* The initial training often involves **Supervised Learning (SL)** on a large dataset of human expert games *before* the **Reinforcement Learning (RL)** phase. This SL phase gives the Policy Network a strong starting point based on human expertise. Then, RL takes over via self-play to refine these networks and potentially discover superhuman strategies. Your description focuses heavily on the DL/RL interplay which is central to its power, but sometimes mentioning the initial SL bootstrap is helpful for completeness. However, your core understanding of the DL/RL synergy and MCTS integration is not impacted by this omission.

**Conclusion:**

Your thinking is very clear, accurate, and demonstrates a deep understanding of the technical workings of AlphaGo, particularly the critical interplay between the neural networks and the search algorithm. You haven't just listed the components; you've explained *how* and *why* they work together, which shows genuine comprehension beyond just reading the tutorial. Well done!"


Thank you,
Amine Moulay Ramdane.