AI Invents New Bowling Techniques
TLDRIn this video, an AI is built using the PPO reinforcement learning algorithm to invent new bowling techniques. The AI, represented as a ragdoll with 12 joints, learns to bowl by optimizing a reward function based on ball speed, lane accuracy, and standing upright. Through several training sessions, the AI explores unconventional strategies like flinging the ball or casting spells. Despite some initial failures and tweaks to the reward system, the AI eventually learns to bowl straight and achieve strikes, although self-preservation remains an issue.
Takeaways
- 🤖 The video features an AI trained using the PPO (Proximal Policy Optimization) algorithm, which was previously used for creating a 'Spider-Man AI.'
- 🎳 The goal of this experiment is to teach the AI how to bowl effectively, starting from learning basic coordination and movement.
- 🧠 The AI is represented as a rag doll with 12 joints and 13 bones, and the challenge lies in rebuilding its coordination from the ground up.
- 🎯 The AI's reward system is designed to incentivize proper bowling behavior by rewarding straight throws and punishing inefficient actions, like falling or throwing the ball off-lane.
- ⚡ Speed is a factor: the AI is rewarded based on the ball's forward velocity, with exponential rewards for faster throws to motivate it to improve.
- 🔄 Despite making progress, the AI initially got stuck in 'local optima,' prioritizing standing or using odd techniques like 'two-step jazz hands' instead of focusing on consistent bowling.
- 🔧 To fix this, the reward function was tweaked to reduce rewards for staying upright, discourage horizontal ball movement, and cap speed rewards to avoid extreme and inaccurate throws.
- 📈 After retraining with the improved reward system, the AI became more effective at throwing straight and even managing to get strikes.
- 🎳 The next challenge is to integrate knowledge of the pins and control over factors like spin, which requires modifying the neural network to handle more inputs and outputs.
- 🧠 The creator plans to perform 'open brain surgery' on the AI by adding new neurons for improved bowling capabilities, aiming for better pin accuracy and spin control in future iterations.
Q & A
What is the main focus of the video?
-The main focus of the video is to demonstrate how an AI can be trained using the PPO algorithm to invent new bowling techniques.
What is PPO and why is it used again in this video?
-PPO stands for Proximal Policy Optimization, which is a reinforcement learning algorithm. It is used again in this video because the presenter finds it beautiful and effective, and wants to showcase its versatility.
What is the AI's initial problem in the bowling scenario?
-The AI's initial problem is that it doesn't know how to walk or perform any actions, which is crucial for bowling.
How many joints and bones does the AI's rag doll body have?
-The AI's rag doll body has 12 joints and 13 bones.
What is the AI's height and weight?
-The AI is six feet tall and weighs about 85 kilos.
What is the purpose of defining a reward function in the AI's training?
-The purpose of defining a reward function is to give the AI an incentive to behave in a certain way, guiding its actions towards desired outcomes.
What are the four components of the reward function described in the video?
-The four components are: 1) Rewarding the AI for keeping the ball within a specific range, 2) Rewarding the AI for the ball's forward speed, 3) Adding an exponent to the speed reward to scale with distance, and 4) Giving a reward proportional to the AI's head's y-coordinate to encourage it to stay high.
What interface does the AI have for controlling its joints?
-The AI is given information about each joint's position, velocity, angular velocity, and the angle it is pointing at, and it has control over the angle it tries to point towards.
What is a local optimum and how does it relate to the AI's performance?
-A local optimum is a solution where the AI gets stuck maximizing a single characteristic of the reward function rather than the overall objective. This is a common issue in reinforcement learning where the AI does not find the best solution to the problem.
What adjustments are made to the reward function to improve the AI's bowling?
-The adjustments include reducing the reward for staying upright, punishing the ball for moving horizontally, and capping the exponential speed reward to encourage straighter and more accurate throws.
Why is adding knowledge of the pins and control over spin considered problematic?
-Adding knowledge of the pins and control over spin is problematic because it requires expanding the neural network with additional inputs and outputs, which could disrupt the network's previously learned behavior.
Outlines
🤖 Reinforcing Spider-Man AI: The New Bowling Challenge
The video starts with a light-hearted introduction, reflecting on a previous project where the creator built a Spider-Man AI using the PPO (Proximal Policy Optimization) algorithm. The creator expresses excitement about reusing this algorithm for a new, playful challenge—training an AI to bowl in a quirky bowling alley setting. The challenge involves controlling a ragdoll-like character with 12 joints and 13 bones, tasked with bowling despite having no initial motor skills. The reward system is crucial for guiding the AI's behavior, and the creator introduces ideas such as rewarding the AI for keeping the ball within a specific range and moving it down the lane, while also avoiding penalties for poor performance. The goal is to teach the AI to bowl with increasing efficiency, though the path to success is uncertain.
🎳 Challenges in Reinforcement Learning: Local Optima and Tweaks
The second paragraph delves deeper into the challenges of reinforcement learning, particularly how the AI struggles with local optima—where it focuses on improving one aspect of bowling (like standing upright) rather than mastering the overall objective of bowling fast and straight. The creator outlines necessary adjustments to the reward function, such as reducing rewards for staying upright, punishing horizontal ball movement, and capping the exponential speed reward to prevent the AI from gaming the system by throwing the ball wildly. These tweaks aim to guide the AI toward a more balanced, effective strategy for consistent bowling.
🧠 AI Surgery and Network Expansion for Better Bowling
In the final section, the creator explains how the AI has made good progress but still lacks key abilities like aiming at the pins and adding spin. To fix this, they propose expanding the neural network by adding extra input and output neurons for better control over the bowling process. This 'open brain surgery' approach is risky, as it could disrupt the AI's previous learning, but the creator hopes that with careful retraining, the AI will be able to improve its bowling performance. Additional rewards will be introduced for knocking down pins, and the process will involve more experimentation to see if the theory works.
Mindmap
Keywords
💡AI
💡PPO
💡Rag doll
💡Reward function
💡Local Optima
💡Neural network
💡Spin
💡Reinforcement learning
💡Interface
💡Optimization
Highlights
Introduction of a new AI built using the PPO algorithm previously used for a Spider-Man AI.
The challenge presented is to knock down 90 pins in a bowling alley, with only 10 pins in the middle being crucial.
The AI character is a rag doll with 12 joints, 13 bones, and unique round feet, making coordination difficult.
The focus is on rebuilding coordination through reinforcement learning, defining a reward system for proper bowling behavior.
The AI receives rewards for keeping the ball within a specific range, with speed and trajectory factored in.
The AI prioritizes standing up over bowling initially, leading to a reset of its learning process.
In later attempts, the AI learns to cast a spell-like motion to get the ball straight but fails due to unrealistic rituals.
A hilarious technique emerges where the AI uses the elasticity in its spine to launch the ball while rearranging its face on the floor.
Despite these amusing techniques, the AI gets stuck in 'local Optima,' maximizing single characteristics rather than overall objectives.
To improve, adjustments are made to the reward system, including reducing rewards for staying upright and punishing horizontal ball movements.
After these tweaks, the AI becomes capable of more consistent strikes, although it still lacks self-preservation.
The video goes beyond basic bowling, aiming to train the AI to control spin and aim, which introduces new challenges.
Due to increased complexity, brain surgery is simulated by adding new neurons to the AI's neural network for better control.
The final solution involves retraining the AI with new inputs and rewards, improving its overall performance.
The experiment concludes successfully with the AI learning to bowl better, though real-world injury prevention remains a secondary concern.
