Training an unbeatable AI in Trackmania
TLDRThis video documents a three-year journey to develop an unbeatable AI for the racing game Trackmania. The AI, powered by a neural network and reinforcement learning, improves over time through trial and error. Despite initial struggles with sub-optimal strategies, the AI eventually surpasses the creator's skills. The video explores the AI's performance on various tracks, its ability to adapt to new challenges, and the creator's attempts to stay competitive. The culmination is a final showdown where the AI's mastery of advanced driving techniques, like neo-drift, secures its victory.
Takeaways
- 🏎️ The AI in Trackmania is designed to improve over time through trial and error, aiming to find the best racing lines and perfect drifting techniques.
- 🧠 The AI utilizes an artificial neural network, a mathematical model inspired by the brain, to make decisions based on inputs from the game state.
- 🔍 Reinforcement Learning is the method used to train the AI, starting from scratch and learning through rewards based on its performance.
- 🏁 The AI's initial attempts are random, but it progressively learns and improves by tweaking its neural network based on the rewards it receives.
- 🚦 The AI's performance is monitored through comparisons with the creator's personal best times on various tracks.
- 🛑 The AI initially struggled with sub-optimal strategies, such as hitting walls, due to conflicts between short-term and long-term rewards.
- 🔧 Debugging and improving the AI involved a trial-and-error loop of adjusting the code, re-training, and waiting for results.
- 🌟 After many adjustments, the AI stopped hitting walls and started approaching the creator's times, indicating significant improvement.
- 🏆 The AI was trained on a more complex track, requiring anticipation of upcoming turns, and showed promising results after several hours of training.
- 💡 The AI's consistency and precision were key to its success, as it was able to complete tracks with fewer mistakes than the human player.
- 🚫 The AI's ability to generalize its learning to new tracks was limited, performing less well on tracks it had not trained on.
Q & A
What is the primary goal of the AI in the game Trackmania?
-The primary goal of the AI in Trackmania is to improve over time through trial and error, with the ultimate aim of finding the best racing lines, drifting perfectly, and becoming unbeatable.
How does the AI in Trackmania use an artificial neural network?
-The AI uses an artificial neural network, a mathematical tool that models how a brain works, to receive inputs about the game state every tenth of a second and output actions to perform, with the aim of finishing the track as quickly as possible.
What is Reinforcement Learning and how is it applied in training the AI for Trackmania?
-Reinforcement Learning is a method where the AI starts with no prior knowledge and makes decisions that are rewarded based on their effectiveness. The AI explores the game, gathers data, and uses this data to progressively tweak the neural network, reinforcing actions that lead to more reward.
Why does the AI sometimes get stuck in sub-optimal strategies?
-The AI can get stuck in sub-optimal strategies due to conflicts between short-term and long-term rewards, which is one of the challenges in reinforcement learning. Initial actions that seem rewarding may later prove to be detrimental.
How does the AI's performance improve over time?
-The AI's performance improves over time through a trial and error loop. Each new attempt allows the AI to explore the game further, gather data, and update its decision process based on fresh knowledge, gradually learning the game until it masters it.
What adjustments were made to the AI's training to help it stop hitting walls?
-To help the AI stop hitting walls, the developer made many small adjustments in the code over time, which eventually led to the AI understanding the game better and avoiding walls.
How does the AI's approach differ when trained on a more complex track?
-On a more complex track, the AI's training method remains the same, but it receives additional inputs to encode the map path for the next three corners, allowing it to anticipate upcoming turns and navigate the non-repetitive road layout.
What is the significance of the AI's ability to generalize its learning to new tracks?
-The AI's ability to generalize its learning to new tracks is significant because it demonstrates whether the AI can apply its learned skills and strategies to different contexts, which is a key aspect of true learning and intelligence.
Why did the AI choose not to drift even when the brake was enabled during training?
-The AI chose not to drift even with the brake enabled because it found a faster strategy that didn't involve drifting. The AI's decisions are based on what yields the highest rewards, and in this case, not drifting was more effective.
How does the AI learn to perform the neo-drift technique in Trackmania?
-The AI learns to perform the neo-drift technique by being given a reward bonus whenever it drifts, detected through a specific input. This encourages the AI to explore and eventually master the drift, which it then uses strategically to save time.
What was the final outcome of the AI's training, and did it beat the human player?
-After three years of training and adjustments, the AI became unbeatable on the first two levels of the game, outpacing the human player in both precision and consistency. On a third, more complex level, the AI was also faster, but its lines were not optimal, suggesting that better human players might still outperform it.
Outlines
🤖 AI Learning to Drive in Trackmania
The AI in the game Trackmania is designed to improve through trial and error, with the goal of mastering the game's mechanics to the point of becoming unbeatable. The narrator has spent years trying to develop an AI that could beat him. Six months ago, he gave the project another try, hoping to give the AI one last chance at redemption. The video follows this journey, explaining how the AI uses a neural network and reinforcement learning to gradually improve its performance.
🚗 Reinforcement Learning and AI Trials
The AI learns through reinforcement learning, a method where it starts without any knowledge, and its actions are rewarded based on how well they perform in the game. At first, its behavior is random, but over time, it adjusts its decision-making process by learning from its successes and mistakes. However, the AI can get stuck in sub-optimal strategies, such as frequently hitting walls due to short-term rewards overshadowing long-term consequences. The narrator experiences many challenges, debugging and refining the AI’s behavior to make it more effective.
🛠 Simplified Training for AI Progress
To speed up training, the narrator simplifies the track and AI's decision-making by disabling braking. After numerous tweaks and training sessions, the AI begins to outperform its previous versions and gets closer to the narrator's personal best. Eventually, the AI surpasses the narrator’s time on the simple track, marking a significant milestone in its development. This victory motivates the narrator to test the AI on more complex maps, though the AI still has room for improvement in generalization and consistency across different tracks.
🏎 Braking and Drifting in Advanced AI Training
The narrator enables braking to see if the AI can improve even further. He trains the AI on more challenging maps and, despite the advantage of braking, the AI still beats his new record by over 5 seconds. While the AI performs impressively, it struggles on new maps, where it makes more mistakes, particularly on long straight paths. These observations highlight the challenges of generalization in AI training, as well as the limitations of the AI’s learning on unfamiliar tracks. However, on familiar tracks, the AI’s performance is exceptional.
🌀 Neo-Drifting: A New Challenge for AI
The narrator introduces the concept of neo-drifting, a complex trick used in Trackmania to save time during races. He adjusts the AI's training by rewarding it for successful drifts, but the AI initially exploits the system by triggering drifting rewards at low speeds. After refining the reward structure, the AI begins to master drifting, learning to use it strategically to improve its times. With this newfound skill, the AI becomes even faster, setting a new record and solidifying its dominance on the endurance map.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Trackmania
💡Reinforcement Learning
💡Neural Network
💡Trial and Error
💡Optimal Strategy
💡Generalization
💡Endurance
💡Neo-drift
💡Patreon
Highlights
AI in Trackmania is designed to improve over time through trial and error.
The AI uses an artificial neural network to model how a brain works.
Reinforcement Learning is used to train the AI, starting from scratch with zero prior knowledge.
The AI explores the game and gathers data, tweaking the neural network to reinforce rewarding actions.
The AI's performance improves as it learns from each attempt, avoiding walls and optimizing its path.
The creator faced challenges in training the AI, including getting stuck in sub-optimal strategies.
The AI's initial tendency to hit walls was due to short-term rewards overshadowing long-term performance.
The creator simplified the decision-making space by using a simple track and disabling brakes.
After many adjustments, the AI stopped hitting walls and got closer to the creator's time.
The AI was trained on a more complex map, requiring anticipation of upcoming turns.
The AI's performance on the second map was initially promising but not robust enough to maintain a lead.
Adding more inputs helped the AI understand the game's physics better.
After 35 hours of training, the AI was faster than the creator on the second map.
The AI was tested on an unseen track, showing good performance but less precision.
The AI's consistency was a key factor in its strength in endurance scenarios.
The AI was retrained with the brake available, leading to slightly faster times but no drifting.
The AI was rewarded for drifting, but initially exploited the reward system with low-speed action patterns.
After further training, the AI mastered the neo-drift and used it wisely to save time.
In the final test, the AI outpaced the creator on a shorter map, showcasing its superior speed and precision.
The creator admits that the AI is unbeatable on the first two levels, but not on the last level.