Speaker 1: Welcome to the deep dive. We're your shortcut to getting informed,
Speaker 1: mixing facts with just enough fun to keep things interesting.
Speaker 1: Today we're jumping into reinforcement learning RL, and it's well
Speaker 1: supercharged version Deep reinforcement Learning or DRL. Our main guide
Speaker 1: for this is the book Deep Reinforcement Learning with Python's
Speaker 1: second edition. It's by Sudharsan Ravichandaran and Villari Babushkin. And
Speaker 1: our mission really is to unpack how AI agents learn
Speaker 1: through interacting and getting rewards. Will explore some applications you
Speaker 1: might not expect, and figure out what makes these learning
Speaker 1: methods just so powerful.
Speaker 2: Yeah, and the really key thing about RL, I think,
Speaker 2: is that the agents learn by actually doing stuff. It's
Speaker 2: not like other machine learning where you just feed it
Speaker 2: a load of data someone already collected. Here, the agent
Speaker 2: is sort of dropped into its world. It has to
Speaker 2: try things out, make choices, and learn directly from the
Speaker 2: consequences from the feedback it gets. It's intelligence that really
Speaker 2: grows through experience.
Speaker 1: Right, learning by doing that trial and error aspect absolutely fundamental.
Speaker 1: So basically, we've got an agent that's the learner in
Speaker 1: an environment, it's world, and in that world there are
Speaker 1: different situations or states. The agent takes actions and then
Speaker 1: gets rewards or I guess sometimes kundlies right. That's the
Speaker 1: feedback exactly.
Speaker 2: The classic analogy, and it really works is teaching a
Speaker 2: dog to catch a ball. You don't sit it down
Speaker 2: with physics diagrams.
Speaker 1: Do you, ah? No, definitely not.
Speaker 2: You just throw the ball. If it catches it, great,
Speaker 2: here's a cookie, positive reward. If it misses, well, no cookie,
Speaker 2: maybe just a neutral outcome. And over lots and lots
Speaker 2: of crows, the dog starts figuring out, Okay, these actions
Speaker 2: in this kind of situation they lead to cookies. It's
Speaker 2: building a strategy really to maximize those treats. That continuous
Speaker 2: loop action, feedback reward in this dynamic world, that's the
Speaker 2: absolute heart of rol.
Speaker 1: That cookie example makes the feedback loop really clear. But
Speaker 1: how is this learn by doing thing really different from
Speaker 1: other kinds of machine learning people might know, like say
Speaker 1: supervised learning.
Speaker 2: Yeah, that's a really important difference. So with supervised learning,
Speaker 2: you're essentially showing the model examples that are already labeled.
Speaker 2: Think of teaching it to spotcats by showing it thousands
Speaker 2: of pictures, and each one clearly says cat or not
Speaker 2: cat got it labeled data right and unsupervised learning that's
Speaker 2: about finding hidden patterns and data that isn't labeled, like
Speaker 2: grouping similar photos together automatically. But RL the agent is
Speaker 2: kind of on its own. It learns by directly messing
Speaker 2: with the environment, changing its behavior based on the feedback
Speaker 2: it gets in real time. There's no pre cooked data
Speaker 2: set of right answers. It has to discover what works
Speaker 2: through this constant back and forth with its world.
Speaker 1: So it's much more dynamic this interaction. Okay, And to
Speaker 1: handle that interaction you need some structure. Right environments need
Speaker 1: to be framed somehow for decision making. What's the usual
Speaker 1: way to do that?
Speaker 2: The standard way, the framework most people use is called
Speaker 2: a Markov decision process or MDP. Basically, it's a mathematical
Speaker 2: way to model these sequential decision problem. It formally defines
Speaker 2: all those bits we mentioned, the states, the possible actions, crucially,
Speaker 2: the probabilities of moving between states when you take an action,
Speaker 2: and the rewards you get for those transitions. The beauty
Speaker 2: of an MDP is it lets us map out almost
Speaker 2: any kind of decision making sequence mathematically, which is how
Speaker 2: machines can start planning strategically.
Speaker 1: Okay, that makes sense. It provides the rules of the game,
Speaker 1: so to.
Speaker 2: Speak, exactly, and a key part of it is the
Speaker 2: Markov property, which sounds complicated, but it just means the
Speaker 2: agent's decision only depends on its current state. It doesn't
Speaker 2: need to remember the entire history of how it got there.
Speaker 2: Just where am I now?
Speaker 1: Right? The present is all that matters for the next decision. Okay.
Speaker 1: So we have the environment structured, but the agent needs
Speaker 1: its own plan, its strategy. How does it figure out
Speaker 1: what to actually do in each state?
Speaker 2: That's its policy. You can think of the policy as
Speaker 2: the agent's rule book or its behavior. It tells the
Speaker 2: agent which action to take when it finds itself in
Speaker 2: a particular state. Policies can be determined meaning in this state,
Speaker 2: always do this specific action, simple okay, Or they can
Speaker 2: be stochastic. This means a state maps to a probability
Speaker 2: distribution over action, so maybe it's seventy percent likely to
Speaker 2: go left thirty percent likely to go right. This allows
Speaker 2: for a bit more randomness, which can be good for exploring. Ultimately,
Speaker 2: the agent is trying to learn the best possible policy,
Speaker 2: the one that gets at the most cumulative reward over
Speaker 2: many runs or episodes. An episode is just one full
Speaker 2: sequence of interaction from start to finish.
Speaker 1: And how does it know if a policy is actually good?
Speaker 1: How does it judge its own strategy?
Speaker 2: Ah? Well, that's where value functions and Q functions come in.
Speaker 2: There are ways to evaluate policies. A value function basically asks,
Speaker 2: starting from this state, how much total reward can I
Speaker 2: expect to get if I follow my current policy? Is
Speaker 2: about the long term value of being in a state?
Speaker 1: Okay, the value of a situation.
Speaker 2: Precisely, and a Q function goes one step deeper. It
Speaker 2: asks how good is it to take this specific action
Speaker 2: when I'm in this specific state and then follow my
Speaker 2: policy afterwards? Okay? The agent uses these calculations to figure
Speaker 2: out which actions in which states are likely to lead
Speaker 2: to the best outcomes down the line.
Speaker 1: Okay, this all sounds really solid, But like you said,
Speaker 1: these agents can be in massive environments thinking about video
Speaker 1: games or robotics. The number of possible states and actions
Speaker 1: must be huge, right, trying to calculate a Q value
Speaker 1: for every single possibility? Yeah, that sounds computationally well impossible.
Speaker 1: How did RL get past that?
Speaker 2: That is exactly the challenge that led to deep reinforcement
Speaker 2: learning or DRL. You're spot on. In complex worlds, you
Speaker 2: just can't compute and store all those Q values there
Speaker 2: are too many. So DRL brings in deep neural networks.
Speaker 2: Instead of calculating exact values, these networks learn to approximate
Speaker 2: the Q function, or sometimes even the policy itself. This
Speaker 2: is the breakthrough that lets RL handle really high dimensional
Speaker 2: inputs like raw pixels from a game screen, which was
Speaker 2: unthinkable before.
Speaker 1: Okay, so neural networks approximate the answers instead of calculating
Speaker 1: everything perfectly. How do these networks learn? What's the mechanism there?
Speaker 2: Well, at a high level, think of a basic artificial
Speaker 2: neural network ANN. You've got layers of interconnected nodes and
Speaker 2: input layer, one or more hidden layers, and an output layer.
Speaker 2: Data flows through gets transformed at each layer, often using
Speaker 2: something called an activation function like RAILU. That's one that
Speaker 2: just outputs zero if the input is negative, and the
Speaker 2: input itself if it's positive. It adds nonlinearity, which is
Speaker 2: crucial now. Learning happens by adjusting the connections, the weights
Speaker 2: and biases within the network. The network makes a prediction,
Speaker 2: say a Q value, we compare that prediction to a
Speaker 2: target value what.
Speaker 1: It should have been.
Speaker 2: The difference is the loss. Then, using calculus tricks like
Speaker 2: gradient descent and backpropagation, the network figures out how to
Speaker 2: tweak its weights and biases to reduce that loss to
Speaker 2: make better predictions next time. It's iterative refinement.
Speaker 1: Got it. So the network learns by correcting its own
Speaker 1: mistakes over and over, and this ability to approximate with
Speaker 1: networks led to some big moments, right, I remember hearing
Speaker 1: a lot about deep Q network.
Speaker 2: Oh. Absolutely. DQN, developed by Google's Deep Mind, was a landmark.
Speaker 2: It was famously used to play a whole suite of
Speaker 2: Atari games, often reaching human level skill just from looking
Speaker 2: at the screen pixels That really grab people's attention.
Speaker 1: Yeah, that was huge. What made it work so well.
Speaker 2: It had a couple of really clever innovations to deal
Speaker 2: with the instability you get when you combine deep learning
Speaker 2: with RL's constantly changing data. First was experience replay. Instead
Speaker 2: of learning only from the very last thing that happened,
Speaker 2: the agent stores lots of past experiences state, action, reward,
Speaker 2: next state in a memory buffer diary exactly, and then
Speaker 2: for training it samples random batches from this memory. This
Speaker 2: breaks up the correlations and sequential data. You know, one
Speaker 2: step often looks a lot like the next, which makes
Speaker 2: the learning much more stable and efficient. It stops the
Speaker 2: network for getting old, useful stuff. The second big idea
Speaker 2: was the target network. They used a separate, slightly older
Speaker 2: copy of the main network just to calculate the target
Speaker 2: Q values. This target net work is held fixed for
Speaker 2: a while, then updated.
Speaker 1: Ah, so the target isn't constantly shifting while the main
Speaker 1: network is trying to.
Speaker 2: Learn precisely, it provides a stable goalpost, preventing the learning
Speaker 2: process from chasing its own tail and diverging. Those two tricks,
Speaker 2: experience replay and target networks were key to dqn's success.
Speaker 1: Okay, so DQN is about learning the values of actions.
Speaker 1: It's value based. Are there other ways to go about it?
Speaker 1: Maybe more direct ways?
Speaker 2: Yes? There are. Another major family of methods are policy
Speaker 2: gradient methods. Instead of figuring out Q values first and
Speaker 2: then working out the policy from those, these methods try
Speaker 2: to learn the optimal policy directly. They adjust the policy
Speaker 2: parameters to favor actions that lead to higher rewards. This
Speaker 2: is often really useful in environments where the actions are continuous,
Speaker 2: continuous like controlling the throttle or steering angle of a car.
Speaker 2: It's not just left, right, up, down, It's a whole
Speaker 2: range of values. Policy gradient methods handle that naturally, often
Speaker 2: using those stochastic policies we mentioned earlier to explore.
Speaker 1: Okay, p learning makes sense for certain problems. Is there
Speaker 1: a way to get the best of both world combine
Speaker 1: value learning and policy learning?
Speaker 2: There is, and that brings us to actor critic methods.
Speaker 2: These are really popular now and form the basis for
Speaker 2: many state of the art algorithms. They essentially have two
Speaker 2: components working together. You have the actor, which is a
Speaker 2: policy network it decides which action to take, and you
Speaker 2: have the critic, which is a value network. It evaluates
Speaker 2: the action taken by the actor, saying hey, that was
Speaker 2: a good move or hmm, maybe not so great. The
Speaker 2: critics feedback then helps the actor update its policy more effectively.
Speaker 2: It's a nice synergy. The actor acts, the critic critiques
Speaker 2: and they both improve together. Algorithms like DDPG TD three
Speaker 2: SAC they're all built on this actor critic.
Speaker 1: Idea actor and critic working together. I like that. Okay,
Speaker 1: before we look ahead, let's maybe touch on a classic
Speaker 1: problem that really highlights a core RL challenge, the multi
Speaker 1: arm bandit.
Speaker 2: AH. Yes, the multi arm bandit or m AB. It's
Speaker 2: simpler than full RL but captures a fun mental trade off.
Speaker 2: Imagine you're in front of several slot machines or bandits,
Speaker 2: each with a lever an arm. You pull an arm,
Speaker 2: you get a payout a reward the catches. Each machine
Speaker 2: cayes out differently with probabilities you don't know beforehand. So
Speaker 2: the big question is do you stick with the machine
Speaker 2: that seems best so far that's exploitation, or do you
Speaker 2: try out other machines hoping to find an even better
Speaker 2: one that's exploration?
Speaker 1: Right, the explorer versus exploit dilemma? How do you balance that?
Speaker 2: There are various strategies, but a common simple one is
Speaker 2: called epsilon. Greedy most of the time, say ninety percent,
Speaker 2: that's one minus epsilon. You exploit by pulling the arm
Speaker 2: of the machine that has given the best average reward
Speaker 2: so far, but with a small probability exelon maybe ten percent.
Speaker 2: You explore by picking an arm completely at random, just
Speaker 2: to see what happens. It's a basic way to ensure
Speaker 2: you don't get stuck on a suboptimal choice forever.
Speaker 1: That's a neat simple way to think about it. Does
Speaker 1: this miib idea show up in the real world outside
Speaker 1: of casinos? Oh?
Speaker 2: Absolutely. It's used all over the place, especially online. Think
Speaker 2: about websites running AB tests for things like which advertisement
Speaker 2: banner gets more clicks. Instead of a fixed AB test,
Speaker 2: a multi armed bandit approach can start showing the better
Speaker 2: performing ad more often even while the test is still running,
Speaker 2: maximizing clicks faster. It also extends to what are called
Speaker 2: contextual bandits. This is where the best arm depends on
Speaker 2: the context like the user. Netflix famously uses this for
Speaker 2: personalizing the thumbnail images for shows and movies based on
Speaker 2: your viewing history. The reward is you clicking play. It's
Speaker 2: also great for cold start problems and recommendations, quickly learning
Speaker 2: what a new user might like.
Speaker 1: Wow. Okay, so that simple banded idea is behind a
Speaker 1: lot of the personalization we see online. That's quite surprising.
Speaker 1: Now let's broaden out again. We've talked games recommendations, But
Speaker 1: where else is RL making a real impact? You mentioned
Speaker 1: the source book covers quite a few areas.
Speaker 2: Yeah, the range is pretty impressive. Now, For instance, dynamic
Speaker 2: pricing businesses use URL agents to adjust prices on the
Speaker 2: fly based on real time supply and demand, trying to
Speaker 2: maximize revenue.
Speaker 1: Like airline tickets are ride sharing apps.
Speaker 2: Exactly like that. Then there's manufacturing training intelligent robots using
Speaker 2: URL to perform tasks like picking and placing objects with
Speaker 2: high precision. This can reduce costs and improve efficiency on
Speaker 2: assembly lines. Finance is another big one. RL is used
Speaker 2: for things like optimizing investment portfolios or developing algorithmic trading strategies.
Speaker 2: JP Morgan, for example, used it to improve how they
Speaker 2: execute large traits for clients, making them more efficient.
Speaker 1: Interesting, so finance, manufacturing, what else?
Speaker 2: Well, there's neural architecture search or NAS that's basically using
Speaker 2: RL to automatically design the structure of other neural networks
Speaker 2: to get the best performance on a task, automating AI
Speaker 2: design with AI, and even in natural language processing NLP,
Speaker 2: people are using RL for tasks like improving abstractive text summarization,
Speaker 2: getting AI to write concise summaries, or making chatbots more
Speaker 2: engaging and goal oriented.
Speaker 1: It really is branching out everywhere. The field. Sound like
Speaker 1: it's moving incredibly fast. Yeah, what's kind of on the hahriz.
Speaker 1: What are the really cutting edge areas right now?
Speaker 2: It is moving fast. Some really exciting frontiers include things
Speaker 2: like meta reinforcement learning. This is about developing agents that
Speaker 2: can learn how to learn, so they get better at
Speaker 2: picking up new tasks quickly because they've learned general learning strategy,
Speaker 2: learning to learn.
Speaker 1: Okay, that sounds powerful. Yeah.
Speaker 2: Then there's hierarchical reinforcement learning or HRL. The idea here
Speaker 2: is to break down really big complex tasks into smaller,
Speaker 2: more manageable sub goals or subtasks. Think about a robot
Speaker 2: needing to make coffee. HRL might break that down into
Speaker 2: go to coverard, get mug, go to machine, press button.
Speaker 2: It makes tackling long horizon problems much more feasible. Like
Speaker 2: the taxi example in the outline, decomposed driving into get
Speaker 2: passenger and drop off passenger makes sense.
Speaker 1: Break it down. Yeah, and you mentioned something earlier that
Speaker 1: sounded almost like AI imagination ah.
Speaker 2: Right, imagination augmented agents or itwo A. This is a
Speaker 2: fascinating direction. These agents try to internally simulate or imagine
Speaker 2: the likely consequences of their actions before actually taking them
Speaker 2: in the real world. It's a bit like how a
Speaker 2: chess player thinks ahead, if I move here, what might
Speaker 2: happen next. They combine learning from actual experience model free
Speaker 2: with learning an internal model of the world to plan
Speaker 2: model based. This allows for more sophisticated planning, especially environments
Speaker 2: where mistakes are costly, like certain puzzle games such as Soacobond,
Speaker 2: which was mentioned in the source.
Speaker 1: Wow. From a dog learning to get a ball with
Speaker 1: cookies all the way to AI agents that can sort
Speaker 1: of imagine the future. That's quite a journey we've covered.
Speaker 1: We've really seen how this core idea of learning through
Speaker 1: trial and error, through rewards and interactions scales up massively
Speaker 1: with deep learning. It lets AI tackle these incredibly complex
Speaker 1: problems in finance, robotics, online systems, you name it. It
Speaker 1: really emphasizes how URL lets agents learn directly adapt on
Speaker 1: the fly. We're probably just scratching the surface of what's.
Speaker 2: Possible absolutely, and maybe a final thought for you to
Speaker 2: consider is just that how that simple principle of learning
Speaker 2: from feedback, which seems intuitive with the dog analogy, scales up.
Speaker 2: It scales to let machines master complex games, manage huge
Speaker 2: financial portfolios, personalize your online world, and even start to
Speaker 2: build internal models to imagine outcomes. Where else could this
Speaker 2: fundamental principle of adaptive reward driven learning take us next?
Speaker 2: What new kinds of dynamic intelligence might emerge,