AI Invents New Bowling Techniques

b2studios
11 May 202311:33
EducationalLearning
32 Likes 10 Comments

TLDRIn this video, the creator revisits the PPO algorithm used for the Spider-Man AI to tackle a new challenge: bowling. After initial failures where the AI focused on standing rather than bowling, adjustments to the reward function led to progress. The AI learned to bowl effectively, though not without some unconventional techniques. Further refinements to the reward system and the addition of new inputs for pin targeting and spin control aim to improve the AI's performance, demonstrating the intricacies and potential of reinforcement learning.

Takeaways
  • 🤖 The video discusses the development of a Spider-Man AI using the PPO (Proximal Policy Optimization) algorithm, which is being repurposed for a new project.
  • 🎳 The new project aims to create an AI that can bowl, specifically targeting knocking over pins in a bowling alley scenario.
  • 🧸 The AI is modeled as a rag doll with 12 joints and 13 bones, with round feet and an abnormal amount of neck strength initially.
  • 📏 The AI's body measurements are set to be six feet tall and 85 kilos, with all body parts having the correct weight for realistic movement.
  • 🎯 A reward function is defined to incentivize the AI to behave in desired ways, such as keeping the ball within a specific range and traveling down the alley.
  • 🚀 The AI is rewarded for the ball's forward speed, with an added exponent to the speed reward to encourage faster throws.
  • 🤹‍♂️ The AI's training sessions show varied results, with initial failures leading to adjustments in the reward function to avoid local optima.
  • 🔄 The video highlights the complexity of reinforcement learning, where AI can get stuck in local optima rather than achieving the overall objective.
  • 🔧 The reward system is tweaked to reduce the reward for staying upright, punish horizontal ball movement, and cap the exponential speed reward.
  • 🏋️‍♂️ After adjustments, the AI becomes more effective at bowling, learning to fall straight and achieve strikes, though self-preservation remains unaddressed.
  • 🔄 Additional features like pin knowledge and spin control are discussed as potential improvements, requiring further neural network adjustments and retraining.
Q & A

  • What algorithm was used to build the Spider-Man AI and how was it applied?

    -The algorithm used to build the Spider-Man AI is called PPO (Proximal Policy Optimization). It was applied to create an agile and floppy AI with a decent amount of brain cells, and it was used again in the video for a different application.

  • What is the main challenge the AI faces in the bowling scenario?

    -The main challenge the AI faces is coordinating its movements to knock over the bowling pins, as it has to learn from scratch how to interact with the physical environment, including handling the bowling ball.

  • 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 are the four components of the reward function defined for the AI to learn bowling?

    -The four components of the reward function are: 1) a reward for keeping the ball within a specific range, 2) a reward proportional to the ball's forward speed, 3) an exponential term added to the speed reward, and 4) a reward proportional to the AI's head's y-coordinate to encourage it to stay upright.

  • What interface information is provided to the AI for each joint?

    -For each joint, the AI is provided with its position, velocity, and angular velocity. The angle information tells the AI where it is pointing, and it has control over the angle it tries to point towards.

  • What was the AI's initial training result?

    -In the initial training, the AI prioritized standing up rather than bowling, which resulted in a complete failure in terms of the bowling task.

  • How did the AI's performance change after the second training session?

    -After the second training session, the AI made decent progress and managed to knock down a few pins, learning a technique similar to casting a spell to get the ball to go straight.

  • What adjustments were made to the reward function to improve the AI's bowling performance?

    -Three adjustments were made: 1) reducing the reward for staying upright, 2) punishing the ball for moving horizontally to improve accuracy, and 3) capping the exponential speed reward to prevent the AI from focusing solely on flinging the ball high.

  • What new aspects were added to the AI's training to improve its bowling accuracy and aim?

    -The new aspects added were control over spin and aiming, which required additional inputs and outputs to the neural network. This was achieved by adding extra input and output neurons and stitching in extra weights to the existing network.

  • What was the outcome of the AI's training with the new reward system?

    -The new reward system was effective, and the AI not only learned to fall straight but also became capable of getting strikes, showing improvement in its bowling performance.

  • What was the AI's final challenge in the bowling scenario?

    -The AI's final challenge was incorporating the knowledge of the pins' location for aiming and adjusting its technique to account for factors like spin, which required additional inputs and outputs to the neural network.

Outlines
00:00
🤖 Building on PPO - Ragdoll Bowling AI

The video begins with the creator's intention to leverage the PPO algorithm again after its successful application in building the Spider-Man AI. The focus shifts to a creative task of developing a ragdoll AI that can bowl, with a unique setup of an old fish tank and a questionable menu at a bowling alley. The AI's body structure is discussed, including its 12 joints, 13 bones, round feet, and correct weight distribution. A humorous note is made about the AI's abnormal neck strength, which is quickly fixed. The creator then delves into defining a reward function to incentivize the AI's desired behavior, such as keeping the ball in the lane and encouraging forward motion. The idea of rewarding the AI based on the ball's speed and adding an exponent to the speed reward is introduced to promote faster throws. The AI's interface is also defined, detailing the inputs and outputs for each joint and the decision-making process for ball release. The video concludes with the AI's initial attempts at bowling, showcasing a progression from standing up to knocking down pins, despite some unconventional techniques and a focus on self-preservation over aiming accurately.

05:02
🎳 Challenges and Tweaks in AI Bowling

This paragraph discusses the challenges faced during the AI's training sessions, where the AI gets stuck in local optima, focusing on single characteristics of the reward function rather than the overall objective of bowling effectively. The creator identifies the need for adjustments to the reward system to guide the AI towards better performance. These adjustments include reducing the reward for staying upright, punishing horizontal ball movement, and capping the exponential speed reward to prevent the AI from prioritizing height over accuracy. The creator then describes the next steps, which involve retraining the AI with a new reward system. The results are promising, with the AI learning to fall straight and achieve strikes. However, the AI lacks knowledge of aiming for the pins, and the creator acknowledges the need to incorporate additional factors such as spin into the training, despite the challenges of expanding the neural network and retraining the AI.

10:36
🚀 Improvisation and Open 'Brain Surgery'

In the final paragraph, the creator discusses the improvisational approach to enhancing the AI's bowling skills by adding more inputs and outputs to the neural network without a clear plan, likening it to performing open brain surgery. The idea is to introduce extra input and output neurons into the network and stitch in additional weights, which should allow the AI to learn to bowl better with minimal retraining. The creator proposes giving extra rewards for knocking pins over, an aspect that was overlooked initially. The paragraph ends on a hopeful note, suggesting that the AI might be able to incorporate these new elements and improve its bowling performance, despite the inherent risks and uncertainties in this improvisational approach.

Mindmap
Keywords
💡PPO (Proximal Policy Optimization)
PPO is a type of reinforcement learning algorithm that aims to train agents by optimizing a stochastic policy—i.e., a policy with a degree of randomness. In the context of the video, PPO is used to train an AI to bowl in a simulated environment, with the algorithm helping the AI learn from its actions and improve over time. The script mentions using PPO again for its effectiveness, indicating its importance in achieving the AI's learning objectives.
💡Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. In the video, the AI is trained using reinforcement learning to bowl, where the reward function guides the AI's behavior towards achieving the goal of knocking down pins. The process involves trial and error, with the AI learning from its successes and failures.
💡Reward Function
A reward function in reinforcement learning is a function that maps situations to a numerical value, indicating how good or bad it is for the agent to be in that situation. In the video, the reward function is carefully designed to encourage the AI to keep the ball in the lane, throw it forward, and eventually knock down the pins. The reward function is crucial as it directly influences the AI's learning process and its ability to achieve the desired behavior.
💡Neural Network
A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In the video, the neural network is trained using the reinforcement learning algorithm PPO to enable the AI to learn how to bowl. The neural network is the core of the AI's decision-making process, learning to adjust its actions based on the rewards and punishments from the reward function.
💡Local Optima
Local optima are points in the solution space of an optimization problem where the objective function decreases in all neighboring points, but is not the point where the function has the lowest possible value. In the context of the video, the AI gets stuck in local optima, meaning it finds a strategy that works well within a limited context but does not lead to the best overall performance in bowling. The AI focuses on maximizing a single characteristic of the reward function rather than achieving the overall goal.
💡Interface
In the context of the video, the interface refers to the set of inputs and outputs that the AI receives and controls. This includes the position, velocity, and angular velocity of each joint, as well as the angle it points at. The interface is crucial for the AI to interact with its environment and make decisions based on the information it receives.
💡Elasticity
Elasticity, in the context of the video, refers to the flexible and stretchable properties of the AI's body, specifically its spine and legs. The AI uses the elasticity in its body to launch the ball, demonstrating a creative use of its physical properties to interact with the environment and achieve the goal of bowling.
💡Training Session
A training session in the video refers to a period of time during which the AI is allowed to interact with the environment and learn from its experiences. These sessions are critical for the AI to improve its performance in bowling, as it learns from its successes and failures. The video describes multiple training sessions, each resulting in different levels of progress towards the goal.
💡Neural Network Surgery
Neural network surgery is an informal term used in the video to describe the process of modifying a trained neural network by adding new input and output neurons, and then retraining the network to accommodate these changes. This is a complex process that requires careful adjustment to ensure the original functionality of the network is preserved while new capabilities are added.
💡Spin
In bowling, spin refers to the rotational movement imparted to the ball, which affects its trajectory and interaction with the pins. In the video, the AI learns to control the spin of the ball, which is a critical aspect of achieving better bowling performance. This addition to the AI's capabilities allows it to not only move the ball forward but also to aim and knock down the pins more effectively.
💡Open-AI
OpenAI is an artificial intelligence research lab that focuses on ensuring that artificial general intelligence (AGI)—highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. In the context of the video, OpenAI is not directly mentioned, but the term 'AI' is used throughout, and the video's content is related to AI development and training, which aligns with OpenAI's mission of safe and beneficial AI development.
Highlights

The video discusses the creation of a Spider-Man AI using the PPO algorithm, which is being repurposed for a new project.

The AI is designed to play bowling, with the objective of knocking down pins in a virtual environment.

The AI's body is described as a rag doll with 12 joints and 13 bones, with round feet and an abnormal amount of neck strength.

The AI's measurements are set to six feet tall and 85 kilos, with all body parts having the correct weight for realistic movement.

A reward function is defined to incentivize the AI to behave in desired ways, such as keeping the ball in a specific range and moving it forward.

The AI is rewarded for the speed of the ball, with an exponential factor added to increase the reward for faster throws.

The AI's interface is defined, with each joint receiving information on position, velocity, and angle, and the AI controlling the angle it points towards.

The initial training session results in the AI prioritizing standing up over bowling, leading to a complete failure.

In the second session, the AI makes progress by learning to cast the ball straight, like a witch.

The final session showcases the AI using its spine and legs' elasticity to launch the ball, demonstrating an innovative technique.

The AI gets stuck in local optima, maximizing single characteristics of the reward function rather than the overall objective of bowling fast and straight.

Tweaks to the reward function are proposed, including reducing the reward for staying upright and punishing horizontal ball movement.

An exponential speed reward is capped to prevent the AI from focusing solely on flinging the ball high without regard to accuracy.

The new reward system is effective, producing an AI capable of falling straight and getting strikes in bowling.

The AI lacks knowledge of the pins and control over spin, necessitating additional inputs and outputs for the neural network.

The video ends with the AI's creator considering open brain surgery on the neural network to incorporate the new requirements.

Transcripts

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