Speaker 1: Welcome to the deep dive. We take source materials, unpack
Speaker 1: complex topics and basically give you the crucial insights and
Speaker 1: maybe some surprising facts along the way.
Speaker 2: Yeah, think of it as your shortcut to getting up
Speaker 2: to speed exactly.
Speaker 1: So today we're diving into some excerpts from a book
Speaker 1: AI Crash Course. We're looking specifically at chapters covering reinforcement learning,
Speaker 1: deep learning, and AI in general.
Speaker 2: Right, and this source it positions itself as a kind
Speaker 2: of all in one guide. It's built from online courses
Speaker 2: that were apparently quite successful, okay, and it really stresses
Speaker 2: getting the intuition first, then the math, and then you know,
Speaker 2: actually coding things.
Speaker 1: Up right, intuition first. So our mission here is to
Speaker 1: pull out those core ideas, help build that intuitive feel,
Speaker 1: and look at some of the well pretty exciting real
Speaker 1: world applications they talk about. The idea is to help you,
Speaker 1: the listener, understand how these AI models actually work and importantly,
Speaker 1: where they might be used.
Speaker 2: When the book sets a big stage, it talks about
Speaker 2: AI's potential impact across well almost every transport, education, security, jobs, entertainment.
Speaker 1: Even the environment, So a lot of potential there.
Speaker 2: Definitely. It frames these technologies as potentially transformative.
Speaker 1: Okay, let's unpack this. Where does our source material suggest
Speaker 1: we start when building an AI, especially in this reinforcement
Speaker 1: learning space.
Speaker 2: It starts with the absolute foundation. You have to define
Speaker 2: the AI's environment.
Speaker 1: The environment.
Speaker 2: Yeah, that's the world, the context the AI operates in.
Speaker 2: And it's got three really key parts. All right, what
Speaker 2: are those first up? States? States are basically the inputs
Speaker 2: the AI gets what it perceives.
Speaker 1: Like sensor readings for a self driving car.
Speaker 2: Maybe exactly, sensor reading speed, location, or for a simple
Speaker 2: robot in a maze, the state might just be which
Speaker 2: square is currently in It's the where am I? Or
Speaker 2: what's happening info?
Speaker 1: Okay, so state is what the AI knows about its situation.
Speaker 1: What's next?
Speaker 2: Next are the actions. These are the things the AI
Speaker 2: can do, the choices it can make.
Speaker 1: So for the car, turn left, accelerate, break.
Speaker 2: Yep, or for the maze robot move north south east west,
Speaker 2: those are its possible moves, its decisions makes sense.
Speaker 1: State action, and the third piece must be important.
Speaker 2: Critically important rewards. This is the feedback what the AI
Speaker 2: gets after it it takes an action in a certain state.
Speaker 1: Ah, the feedback loop.
Speaker 2: Precisely, it could be positive like reaching a goal, or
Speaker 2: negative like hitting a wall. This reward signal is what
Speaker 2: guides the AI. It tells it what's good and what's bad.
Speaker 1: So the whole game for the AI is to figure
Speaker 1: out how to act, which actions to take in which
Speaker 1: states to get the most reward possible over time.
Speaker 2: That's the core idea, maximize cumulative reward. It learns by
Speaker 2: essentially trial and error driven by those rewards. And the
Speaker 2: source makes this important distinction too, between training mode and inference.
Speaker 1: Mode, right. Training versus inference.
Speaker 2: Training is where it's learning, interacting, getting rewards, updating it
Speaker 2: to understanding. For instance, well at showtime, the trained AI
Speaker 2: uses what it learned to just do the task without
Speaker 2: learning anymore.
Speaker 1: Got it learn first, then perform. So with that framework
Speaker 1: state's actions rewards, what's like the first actual AI model
Speaker 1: the book introduces.
Speaker 2: It kicks off with the classic problem, actually the multi
Speaker 2: arm banded problem.
Speaker 1: Ah, the slot machines. I remember this.
Speaker 2: One, yeah, exactly, multiple slot machines bandits in a casino.
Speaker 2: Each pays out with a different probability, but you don't
Speaker 2: know those probabilities.
Speaker 1: So the question is, how do you play them to
Speaker 1: maximize your winnings over time without knowing which machine is
Speaker 1: actually best initially exactly.
Speaker 2: And the AI approach discussed is Thompson sampling.
Speaker 1: Thompson sampling. Okay, what's the intuition there?
Speaker 2: Sound statistical, it is, but the intuition is quite neat.
Speaker 2: You could just keep playing the machine that's paid out
Speaker 2: the most.
Speaker 1: So far, right, Yeah, seems logical.
Speaker 2: Exploit the winner, but that winner might just be on
Speaker 2: a lucky streak.
Speaker 1: Thompson sampling is smarter. It keeps track of wins and
Speaker 1: losses for each machine, okay, and uses that history to
Speaker 1: maintain a probability distribution for each machine's likely success rate,
Speaker 1: specifically a beta distribution.
Speaker 2: A beta distribution, so more wins on a machine means
Speaker 2: its distribution shifts towards predicting higher success Right.
Speaker 1: More wins, fewer losses, the distribution gets more confident that
Speaker 1: the machine is good. Now here's the clever bit. Each round,
Speaker 1: you don't just pick the machine with the highest average
Speaker 1: win rate so far.
Speaker 2: No, we then you take a random draw from each
Speaker 2: machine's beta distribution, and you play the machine whose random
Speaker 2: draw came out highest for that round.
Speaker 1: Random draw? Why random seems like you'd want the most
Speaker 1: likely winner.
Speaker 2: That randomness is key. It builds an exploration. A machine
Speaker 2: you haven't played much will have a wider, less certain distribution,
Speaker 2: so its random draws might sometimes be high, prompting you
Speaker 2: to try it out.
Speaker 1: Ah, so it forces you to explore the less known options,
Speaker 1: sometimes just in case they're actually better than the current favorite.
Speaker 2: Exactly. It naturally balance is exploring new things, exploration with
Speaker 2: sticking to what seems to work exploitation, and it does
Speaker 2: this just based on the observed wins and losses, without
Speaker 2: needing the true payout rates.
Speaker 1: That's really clever. Balancing exploration and exploitation is a classic problem.
Speaker 1: Does the book give a real world example?
Speaker 2: Yes, a great one. Online advertising, which adversion gets the
Speaker 2: most clicks or sign ups?
Speaker 1: Okay, so each AD variation is like a slot machine.
Speaker 2: Arm perfect analogy. You show different ads, oh actions, you
Speaker 2: track clicks or conversions rewards one for click, zero for
Speaker 2: no click. Thompson sampling figures out which ad performs best over.
Speaker 1: Time by showing ads somewhat randomly based on those beta distributions.
Speaker 1: Learning as it goes.
Speaker 2: Yep, it converges on the statistically best ad adapting as
Speaker 2: it gets more data maps directly from the casino problem.
Speaker 1: Pretty neat, very neat, Okay, So Thompson sampling helps pick
Speaker 1: the best single option. But many problems involve a sequence
Speaker 1: of actions to reach a.
Speaker 2: Goal, right, and that's where the source introduces Q learning.
Speaker 2: This is a really foundational reinforcement learning algorithm for sequential decisions.
Speaker 1: Q learning. What's the Q stand for?
Speaker 2: It stands for quality. Essentially, the core idea is the
Speaker 2: Q value.
Speaker 1: Okay, quality value. What does it represent?
Speaker 2: A Q value written q s is a number. It
Speaker 2: represents the expected total future reward you'll get if you
Speaker 2: take action A when you're in state S A and D.
Speaker 2: This is key you act optimally after that.
Speaker 1: Whoa Okay, So it's not just the immediate reward for
Speaker 1: taking action A, it's that plus the best possible rewards
Speaker 1: you could get from then on.
Speaker 2: Exactly. It's the long term value of taking that specific
Speaker 2: action in that specific state. The goal of Q learning
Speaker 2: is to learn these Q values for all possible state
Speaker 2: action pairs.
Speaker 1: So if you know all the Q values, you just
Speaker 1: pick the action with the highest Q value in your
Speaker 1: current state, and that's the best move.
Speaker 2: That's the idea for using it once it's learned. Yes,
Speaker 2: but how does it learn those values? You use something
Speaker 2: called a temporal difference or TD.
Speaker 1: Temporal difference sounds like difference over time.
Speaker 2: Kind of think of TD as measuring the surprise. It's
Speaker 2: the difference between the AI's current estimate of qs A
Speaker 2: and a better estimate it gets after actually taking action A,
Speaker 2: getting a reward R, and seeing the next state's prime.
Speaker 1: How does it get that better estimate?
Speaker 2: The better estimate is the immediate reward R plus the
Speaker 2: maximum Q value it could get from that next state's
Speaker 2: prim basically R plus max QS.
Speaker 1: Okay, so TD is actual reward plus best future value
Speaker 1: from next state minus my old estimate of current state
Speaker 1: action value.
Speaker 2: You've got it. A big positive TD means Wow, that
Speaker 2: action was way better than I thought. A negative TD
Speaker 2: means Oops, that was worse.
Speaker 1: And this TD error is used to update the original
Speaker 1: Q value estimate.
Speaker 2: Precisely using the Bellman equation, which is the mathematical rule
Speaker 2: for this update. It uses the TD error and a
Speaker 2: learning rate to nudge the Q value closer to that
Speaker 2: better estimate. It links the immediate reward to the future potential.
Speaker 1: So it learns iteratively. Can you walk through the training
Speaker 1: process generally? Sure.
Speaker 2: You start by initializing all all Q values, maybe to zero.
Speaker 2: Then you run many episodes. In each episode, maybe started
Speaker 2: a random state, pick a random valid action. See what
Speaker 2: reward you get in what state you land in?
Speaker 1: Okay?
Speaker 2: Then you calculate that TDR based on the reward and
Speaker 2: the max q value of the next state, and you
Speaker 2: update the Q value for the state action pair you
Speaker 2: just experienced, repeat, repeat, repeat.
Speaker 1: Lots of exploration and updating exactly.
Speaker 2: Over time, exploring the environment and propagating these rewards back
Speaker 2: via the TV updates, the Q values start to converge
Speaker 2: towards the true optimal values.
Speaker 1: And then once training is done, The inference process is.
Speaker 2: Simple, very simple. Put the AI in any state s
Speaker 2: it looks up the learned Q values for all possible
Speaker 2: actions A from that state. It picks the action with
Speaker 2: the highest Q value. That's its policy.
Speaker 1: Okay, that makes sense. It learns the map of values,
Speaker 1: then follows the path of highest value. The source gives
Speaker 1: a warehouse robot example, right.
Speaker 2: Yeah, a really clear one. Guiding a robot through a
Speaker 2: maze like a warehouse layout to get to a specific
Speaker 2: goal location, say location G.
Speaker 1: How does that map to states, actions rewards.
Speaker 2: The states are just the robot's current location ABC. The
Speaker 2: actions are moving to an adjacent connected location, simple enough,
Speaker 2: and the rewards are designed to get it to G.
Speaker 2: Maybe a small reward like plus one for any valid
Speaker 2: move between locations, zero reward if it tries to move
Speaker 2: through a wall and the goal. A big reward, say
Speaker 2: plus one thousand for reaching location G. That high value
Speaker 2: at the goal is the incentive.
Speaker 1: So during training, the robot wanders around, bumping into walls,
Speaker 1: maybe stumbling into G. Eventually right, and.
Speaker 2: When it gets rewards, especially that big one of G,
Speaker 2: the TD updates start propagating that value backwards along the
Speaker 2: paths leading to G.
Speaker 1: So actions that lead towards G gradually get higher Q.
Speaker 2: Values Exactly the Q values effectively learn the goodness of
Speaker 2: each move in terms of reaching.
Speaker 1: The goal and the sourt's also mentioned. You could add
Speaker 1: intermediate goals like forcing the robot to go through location
Speaker 1: K on the way to G.
Speaker 2: Yes, you just tweak the reward matrix give a medium
Speaker 2: sized reward, maybe five hundred specifically for the action of
Speaker 2: moving from jda K if that's the desired intermediate step.
Speaker 1: AH make that specific transition valuable.
Speaker 2: Or you could add a big negative reward main to
Speaker 2: five hundred for a transition you wanted to avoid, like
Speaker 2: going from jda F. You shape the desired path by
Speaker 2: manipulating the rewards for specific state action.
Speaker 1: Pairs, very flexible and in inference. The trained robot would
Speaker 1: then follow the path that accumulated the highest.
Speaker 2: Q values testing the example path mentioned E to I
Speaker 2: to JDAK than LHG. The robot figures that out just
Speaker 2: by following the highest Q value at each step, guided
Speaker 2: by the rewards you designed.
Speaker 1: Okay, Q learning seems powerful for these kinds of discrete
Speaker 1: state spased problems. But what about more complex stuff like
Speaker 1: dealing with messy continuous data or images.
Speaker 2: Exactly, that's the limit of basic Q learning tables. For
Speaker 2: more complex problems. The source brings in artificial neural networks
Speaker 2: an ns and deep learning. The artificial brains kind of yeah,
Speaker 2: inspired by biologic brains. The basic unit is the neuron.
Speaker 2: It gets inputs, multiplies them by weights, sums them.
Speaker 1: Up, and passes the result through an activation function like
Speaker 1: re lu the rectifier you mentioned right.
Speaker 2: That activation function adds nonlinearity, which is super important for
Speaker 2: learning complex patterns. These neurons are arranged in layers input,
Speaker 2: hidden layers, output information flows forward.
Speaker 1: Okay, and how do these networks learn you mentioned adjusting weights.
Speaker 2: They learn by trying to minimize error. For example, predicting
Speaker 2: house prices, the network makes a prediction you compare to
Speaker 2: the actual price.
Speaker 1: That difference is the loss error, and it tries to
Speaker 1: reduce that error.
Speaker 2: Yes, using optimization algorithms like gradient descent, it calculates how
Speaker 2: adjusting each weight would affect the error and nudges the
Speaker 2: weights in the direction that reduces the error. Or many many.
Speaker 1: Examples the book uses that house price prediction example. What's
Speaker 1: a really critical step when you feed data like house size,
Speaker 1: number of bedrooms, et cetera into an ann.
Speaker 2: Data prep is huge. Splitting into twenty two test sets
Speaker 2: is standard, But the crucial thing, especially for an n's
Speaker 2: is scaling the data.
Speaker 1: Scaling Why is that so vital?
Speaker 2: Imagine number of bedrooms maybe one to five versus square
Speaker 2: footage thousands. Without scaling, the network might overweight square footage
Speaker 2: just because the numbers are bigger. Even if bedrooms are
Speaker 2: just as important.
Speaker 1: Ah, the scale of the numbers dominates the learning.
Speaker 2: Exactly scaling methods like midmax scale are mentioned in the
Speaker 2: source bring all features into a similar range like zero
Speaker 2: to one. So the network learns based on the predictive
Speaker 2: power of each feature, not just its raw numerical size.
Speaker 1: Makes sense, leveling the playing field for the input features. Okay,
Speaker 1: so we have Q learning for sequences ANNs for complex data.
Speaker 1: What happens when you put them.
Speaker 2: Together, magic happens. That's deep Q learning or DQN. This
Speaker 2: is where things get really powerful for complex RL problems.
Speaker 1: Deep Q learning. So the deep comes from the deep
Speaker 1: learning neural network exactly.
Speaker 2: The an N acts as a function approximator for the
Speaker 2: Q function instead of a giant table storing queues A
Speaker 2: for every possible state in action, which is impossible for
Speaker 2: complex environments.
Speaker 1: Right the state space could be enormous or even continuous.
Speaker 2: The ANN takes the states as as input, and its
Speaker 2: output layer predicts the Q values for all possible actions
Speaker 2: A from that state.
Speaker 1: So the network learns to estimate the Q values on
Speaker 1: the fly based on the input state.
Speaker 2: Precisely. It generalizes. Now, when it comes to choosing an
Speaker 2: action during training, DQN doesn't always just pick the action
Speaker 2: with the highest predicted Q value. That would be pure exploitation.
Speaker 1: It needs exploration too, write like in Thompson sampling exactly.
Speaker 2: The source mentions common strategies like softmax or epsilon greedy
Speaker 2: exploration epsilon greedy.
Speaker 1: That's the one where, say ten percent of the time,
Speaker 1: it just picks a random action instead of the best one.
Speaker 2: Yeah, that's the idea with probability upslone explore randomly, otherwise
Speaker 2: exploit the best known action. Softmax assigns probabilities based on
Speaker 2: Q values, giving even weaker actions some chance. This exploration
Speaker 2: is crucial for discovering potentially better strategies the AI doesn't
Speaker 2: know about yet.
Speaker 1: Okay, so how does the DQN actually learn? How does
Speaker 1: the network get better at predicting Q values.
Speaker 2: It's similar to the q learning update, but uses the
Speaker 2: network The AI is in state a's picks an action
Speaker 2: A using epslong, greedy you or similar, observes the reward
Speaker 2: R and the next state's prime.
Speaker 1: Okay.
Speaker 2: It then uses the same neural network to predict the
Speaker 2: maximum Q value possible from that next state. Hell, it's prime.
Speaker 2: Let's call that max qs. It calculates the target Q value.
Speaker 2: Target equals R plus gamma max qsaighty gamma is a
Speaker 2: discount factor for future rewards, so.
Speaker 1: Reward plus the discounted best value from the next state
Speaker 1: that's the target right now.
Speaker 2: It compares this target value to the q value the
Speaker 2: network originally predicted for the action a it actually took
Speaker 2: in state. As the difference between the prediction and the
Speaker 2: target is the error, the temporal difference error again.
Speaker 1: And that error signal is used to update.
Speaker 2: The network exactly. The error is backpropagated through the ann
Speaker 2: adjusting the weights so that next time the network's prediction
Speaker 2: for q A will be closer to that target value.
Speaker 2: It learns to make better predictions through experience.
Speaker 1: And there is something about experience replay.
Speaker 2: Memory ah yes, crucial for stability. Instead of learning only
Speaker 2: from the very last thing that happened, the AI stores
Speaker 2: lots of past experiences state action reward next to state
Speaker 2: tipples in a big memory buffer. Okay, Then for learning updates,
Speaker 2: it samples random mini batches of these past experiences.
Speaker 1: From the buffer way random badges.
Speaker 2: It breaks the correlation between consecutive experiences. Learning step by
Speaker 2: step can be unstable because consecutive states are often very similar.
Speaker 2: Random sampling makes the training data more diverse and independent
Speaker 2: in each batch, which really helps stabilize the learning process
Speaker 2: for the deep neural network.
Speaker 1: Got it okay, DQN sounds really powerful. The source must
Speaker 1: have some cool applications. You mentioned virtual self driving.
Speaker 2: Car, Yeah, a great example in the book, they use
Speaker 2: a Kivi app a Python framework to simulate it. The
Speaker 2: input states for the AI are are things like the
Speaker 2: car's angle towards the goal, but also crucially sensor readings,
Speaker 2: what kind of sensors virtual sensors detecting sand basically obstacles
Speaker 2: or off road areas to the left, front and right.
Speaker 2: This gives the AI situational awareness.
Speaker 1: And the actions are simple driving controls.
Speaker 2: Basic steering adjustments.
Speaker 1: Yeah.
Speaker 2: The rewards are set up to encourage driving well, a
Speaker 2: penalty magnetive one for hitting sand borders, a smaller penalty
Speaker 2: need you a point two if it moves away from
Speaker 2: the goal, and a small reward plus point one from
Speaker 2: moving towards the goal.
Speaker 1: So the DQN learns to process those sensor inputs, predict
Speaker 1: Q values for steering actions, and chooses actions that avoid
Speaker 1: penalties and get rewards.
Speaker 2: Exactly, it learns through trial and error in the simulation
Speaker 2: to stay on the road, avoid sand and navigate towards
Speaker 2: the target, eventually making round trips. You use something like
Speaker 2: py torch or TensorFlow to build the an N park
Speaker 2: very cool.
Speaker 1: And the server cooling example that sounded really practical.
Speaker 2: Extremely practical, applying Dkewin to minimize energy costs into server environment.
Speaker 1: So the input states there are things affecting temperature right.
Speaker 2: Server's current temperature, maybe number of active users, data transmission rate,
Speaker 2: factors influencing heat load, and the actions discrete choices. The
Speaker 2: source example use things like cool by one point five
Speaker 2: degrees cools by point five degree C, do nothing, heat
Speaker 2: by one point five degree c, heat by one point
Speaker 2: five degree C. Five distinct actions.
Speaker 1: And the reward is the energy saved compared to a
Speaker 1: standard maybe thermostat based system exactly.
Speaker 2: The goal is purely energy efficiency. The DQAN trains by
Speaker 2: simulating temperature changes based on inputs and its actions, learning
Speaker 2: which sequence of cooling heating actions keeps the temperature within
Speaker 2: an acceptable range while using the least energy possible.
Speaker 1: And it uses a standard A and unset up.
Speaker 2: Yeah, the source mentions a typical structure maybe two hidden
Speaker 2: layers means squared error mc loss to measure how far
Speaker 2: off its temperature prediction? Is the atom optimizer to adjust
Speaker 2: weights and epsilon greedy exploration during training.
Speaker 1: And the result was significant, quite significant.
Speaker 2: Yeah, the source sited achieving up to eighty seven percent
Speaker 2: energy savings compared to the baseline. That's a huge real
Speaker 2: world win from applying URL.
Speaker 1: Wow. Okay, so DQN handles complex states, but what about
Speaker 1: visual states like images or game screens.
Speaker 2: Ah, Now we get to deep convolutional q learning DCQN.
Speaker 2: This brings in convolutional neural networks CNNs.
Speaker 1: CNNs. They're specialized for images, right exactly.
Speaker 2: They're designed to process grid like data and images are
Speaker 2: the prime example.
Speaker 1: How do they work sort of intuitively?
Speaker 2: Well, the first key step is convolution. You slide small
Speaker 2: filters across the image. Each filter is designed to detect
Speaker 2: a specific simple feature like a vertical edge, horizontal edge,
Speaker 2: a corner, maybe a certain texture or color patch. This
Speaker 2: produces feature maps.
Speaker 1: Okay, finding basic patterns, then.
Speaker 2: Codes pooling, often max pooling. It takes small regions of
Speaker 2: the feature map and just keep the maximum value. It's
Speaker 2: a way to downsample reduce the data size will keeping
Speaker 2: the most salient features detected. It makes the network more
Speaker 2: robust to small shifts or distortions.
Speaker 1: So extract features, then condense them right after several layers
Speaker 1: of convolution and pooling, you've extracted increasingly complex features.
Speaker 2: Then you flatten the final two D feature maps into
Speaker 2: a single long one.
Speaker 1: D vector okay, a feature vector, and.
Speaker 2: That vector is then fed into a standard fully connected
Speaker 2: ann like we discussed before for the final prediction or
Speaker 2: decision making in this case predicting Q values.
Speaker 1: And you mentioned CNNs can handle three D inputs. That's
Speaker 1: important for.
Speaker 2: For the next example. Yeah, playing the classic Snake game
Speaker 2: using dcqn.
Speaker 1: Ah Snake perfect visual task exactly.
Speaker 2: The state isn't just a single snapshot of the game screen.
Speaker 2: To understand movement, the AI needs context, So the state
Speaker 2: input is actually a stack of recent game frames.
Speaker 1: Like layering the last few frames together precisely.
Speaker 2: Think of it like a three D volume with height
Speaker 2: and a short time dimension. This allows the CNN to
Speaker 2: perceive motion and direction, not just static positions.
Speaker 1: That's clever, Okay. What are the actions for snake?
Speaker 2: Simple up, down, left, right for possible moves?
Speaker 1: What about impossible moves like if the snake is going right,
Speaker 1: it can immediately go left.
Speaker 2: Good? Point the AI might try to command left, the
Speaker 2: game engine ignores it. The snake continues right and promptly dies.
Speaker 2: The key is the AI takes the action left, observes
Speaker 2: the outcome death, gets a negative reward, and associates that
Speaker 2: negative reward with the attempted action left. In that specific
Speaker 2: state moving right next to self, it learns trying to
Speaker 2: go left here is bad.
Speaker 1: Ah okay, It learns the consequence of the attempted action,
Speaker 1: even if the game rules prevent it. What about the rewards.
Speaker 2: Simple and effective plus one for eating an apple, negative
Speaker 2: one for dying, hitting wall or self, and crucially, a
Speaker 2: small negative reward like negative point zero three for every
Speaker 2: single step that doesn't end the game.
Speaker 1: Or get an apple a living penalty. Why penalize it
Speaker 1: just for moving.
Speaker 2: To encourage efficiency. Without it, the snake could just wiggle
Speaker 2: around an empty space forever. Not dying is okay. Not
Speaker 2: getting apples as okay. That small penalty incentivizes it to
Speaker 2: find apples quickly, because eating an apple plus one is
Speaker 2: the main way to counteract the accumulating negative living penalty.
Speaker 1: Makes sense, drives it towards the objective efficiently. So the
Speaker 1: DCQN takes the stack frames, processes them through CNN layers
Speaker 1: to understand the visual state where's the snake, apple Wall's.
Speaker 2: Body, flattens those features, feeds them to an ANN, which
Speaker 2: outputs the Q values for up, down, left, right, and.
Speaker 1: It learns by taking actions with exploration, getting rewards, penalties,
Speaker 1: and updating the whole network CNN plus ANN via backpropagation
Speaker 1: using the TD.
Speaker 2: Error You've got it and the result mentioned the source.
Speaker 2: After training, the AI could consistently eat around ten to
Speaker 2: eleven apples per game, which is pretty decent for learning
Speaker 2: from scratch.
Speaker 1: That's really cool, So quite a journey there from the
Speaker 1: absolute basics of.
Speaker 2: RL state's actions reward Drew.
Speaker 1: Thompson sampling for simple choices, Q learning for basic sequences.
Speaker 2: Then bringing in the power of neural networks with DQN
Speaker 2: for complex.
Speaker 1: States handling things like driving and server cooling, and.
Speaker 2: Finally DCQN use and convolutional networks to actually see and
Speaker 2: play a game.
Speaker 1: Like Snake Yeah, across casinos, warehouses, cars, server rooms, video games.
Speaker 1: It's amazing how that core loop of interaction and reward applies.
Speaker 2: And the source really hammers home that idea of intuition
Speaker 2: first practice, continuous learning. It even mentions resources like opening
Speaker 2: EYEGM for getting hands on.
Speaker 1: Right because understanding is one thing, but actually building these things.
Speaker 2: That takes practice. And thinking back to that big picture
Speaker 2: of the book painted at the start, all those potential
Speaker 2: application areas.
Speaker 1: Yeah, it kind of brings it full circle. We've seen
Speaker 1: the build in blocks. Now you can think about where
Speaker 1: they might fit exactly.
Speaker 2: We pulled out the core ideas from these excerpts.
Speaker 1: So what's the big takeaway here for you listening?
Speaker 2: Well, I think it's that these complex AI systems, they
Speaker 2: often boil down to these understandable core principles, defining the
Speaker 2: problem clearly states actions rewards is maybe half.
Speaker 1: The battle, yeah, and then using these learning algorithms, often
Speaker 1: involving neural networks now to figure out the optimal strategy
Speaker 1: through interaction and feedback, whether it's beta distributions for ADS
Speaker 1: or CNNs for snake.
Speaker 2: The potential is just huge and it's evolving so fast.
Speaker 1: So as you think about what we've talked through, the
Speaker 1: MULTIIRN bandit, the warehouse robot, the self driving car simulation,
Speaker 1: the energy saving server, the gameplaying AI, maybe ask yourself, this,
Speaker 1: is there a problem or a task in your world,
Speaker 1: maybe work, maybe a hobby that you could perhaps frame
Speaker 1: in terms of states, actions and rewards.
Speaker 2: How would an AI learning just through trial and error
Speaker 2: and feedback approach solving it. It's a powerful way to
Speaker 2: think about automation and optimization.
Speaker 1: That idea of learning by doing, driven by feedback, it
Speaker 1: really is powerful.
Speaker 2: Definitely something them all over.
Speaker 1: Thanks for diving deep with us today.
Speaker 2: Yeah, great discussion.
Speaker 1: We'll see you on the next one.