"We know that [reinforcement learning] is enough for intelligence because this is the way that all mammals, including humans, learn."
Demis Hassabis
"The goal of reinforcement learning is to provide algorithms that can handle both finding previously unknown solutions and learning quickly in new environments."
"In reinforcement learning, the immediate reward for taking an action might be low or even negative, but it might still be worth it if it leads to a better long-term outcome."
"Optimizing decisions in reinforcement learning involves considering both immediate and future rewards, reflecting a balance between short-term gains and long-term benefits."
"Distributional reinforcement learning explicitly models the distribution of returns, offering a more nuanced understanding of risks and rewards."
"Exploration and exploitation are critical concepts in reinforcement learning, balancing the acquisition of new knowledge and the utilization of known information."
"It's going to be a lecture on games, a case study on classic board games with reinforcement learning."
"In reinforcement learning, there's an agent that's learning in an interactive environment based on rewards and penalties."
"Reinforcement learning is a bit like training a dog."
"Reinforcement learning involves teaching the machine to think for itself based on its past action rewards."
"Reinforcement learning is teaching a software agent how to behave in an environment by telling it how good it's doing."
"It's really interesting that in classical formalisms about RL you have a notion of an explore-exploit trade-off built in..."
"Learning a hierarchy of actions is a promising approach for reinforcement learning."
"Our goal in all of this isn't really about beating humans at Dota; our goal is to push the state of the art in reinforcement learning."
"It's almost like a reinforcement learning exploration but at the scale of humans."
"Combining reinforcement learning with deep learning builds extraordinary applications."
"Despite its remarkable simplicity, in my opinion of essentially trial and error, they tested this on many many games in Atari and showed that for over 50 percent of the games they were able to surpass human level performance with this technique."
"Now let's dive into the details of how policy learning works."
"Creating a general algorithm that learns to master new domains out of the box would overcome a barrier of expert knowledge and open up reinforcement learning to a wide range of practical applications."
"Reinforcement learning is one of the early algorithms that was used to kind of figure out this near optimal scheduling policy."
"Reinforcement Learning is the process of learning in an environment, through feedback from an AI’s behavior, it’s how kids learn to walk!"
"Reinforcement learning... there's an agent that is learning in some sort of interactive environment, based on rewards and penalties."
"Reinforcement learning is all to do with teaching a machine learning model how to interact in order to maximize its outcome."
"Welcome to the Reinforcement Learning Jump Start series."
"You're going to learn everything you need to know to get started with reinforcement learning."
"How can an agent maximize long-term rewards in environments with uncertainties?"
"Q learning works by mapping pairs of states and actions to the future rewards the agent expects to receive."
"What sets Q-learning apart from many reinforcement learning algorithms is that it performs its learning operation after each time step."
"Deep Q learning agents have a memory of the states they saw, the actions they took, and the rewards they received."
"Reinforcement learning is a class of machine learning algorithms that help an autonomous agent navigate a complex environment."
"Reinforcement learning basically boils down to an agent interacting with some environment and receiving some rewards in the process."
"Reinforcement learning agents seek to maximize their total reward but face a dilemma of whether to maximize current reward or take exploratory steps with suboptimal actions in the hope of optimizing long-term rewards."
"This is precisely how the agent learns over time and what makes policy gradient methods so powerful."
"Q-learning is a powerful solution because it lets agents learn from the environment in real time and quickly learn novel strategies."
"Stable Baselines 3 is for reinforcement learning in python like scikit-learn is for general machine learning."
"You can pair this with reinforcement learning algorithms to get algorithms that play video games, learn to play go, or even control robots."
"Most deep reinforcement learning algorithms are going to fail to learn if the data that they're trained on is not heavily correlated with the current policy."
"We wanted to bring together deep learning and reinforcement learning."
"Reinforcement learning trains a machine to take suitable actions and maximize reward in a particular situation."
"Reinforcement learning algorithms are widely used in the gaming industries to build games."
"Reinforcement learning with human feedback: a really powerful concept that starts to make these stable diffusion models much more powerful over time."
"Remember, this is a simulated environment, so our agent doesn't actually know how it accumulates its reward. It just knows that by doing certain actions, it's going to get a reward."
"Reinforcement learning is all about predicting the best type of action."
"Using reinforcement learning, ChatGPT continuously learns from user interactions and rewards, improving over time."
"Reinforcement learning: machines learn from their mistakes, just like we do."
"Reinforcement learning is where there is no data. There's an environment and an ML model generates data and makes many attempts to reach a goal."
"Reinforcement learning fundamentally involves optimization, delayed consequences, exploration, and generalization."
"Thank you very much for your attention. That is how deep reinforcement learning works."
"Reinforcement learning has great applications in many cutting-edge fields such as self-driving cars, robotics, and even winning complicated games like Go, chess, and Atari games."
"...we cheat this error function and we multiply it by four...so that the policy net will be encouraged to take steps with high gain and discourage to take steps with low gain."
"The best way to learn reinforcement learning is to jump in."
"Variational inference has a very deep connection to reinforcement learning and learning-based control."
"This reinforcement learning approach makes everything fit together perfectly."
"So the total P&L at the end of a simulation is the cumulative sum of these immediate rewards."
"So, there's a lot of interesting ways in which you can plug graphs into RL and a lot of them are being explored right now."
"RL agents learn through trial and error by interacting with their environments and attempting to maximize Total Rewards."
"Reinforcement learning is built around the concept of a Markov decision process, or MDP, which is a way to model decision."
"Reinforcement learning has an environment and an agent, and the agent is basically performing some actions in order to achieve a certain goal."
"Reinforcement learning is that you can build really sophisticated complex machine learning models with no training data."
"If you're trying to optimize your supply chain or figure out the best treatment for cancer, reinforcement learning is incredibly valuable."
"Reinforcement learning is incredibly valuable. Think about if you're trying to optimize your supply chain or figure out the best treatment for cancer."
"With reinforcement learning, you define the reward function and the algorithm iterates until it finds the optimal way to do it."
"Multi-armed bandits are really reinforcement learning problems with a single state."
"The exploration-exploitation problem is a fundamental problem in reinforcement learning."
"If you follow that special policy, this PI star, at any given state, you're guaranteed that there exists no other policy under which you, on expectation, get a better sum of discounted rewards."
"G of T is the return, and as we note here, this dot just means that this is a definition."
"The return can be written as the first reward plus gamma times the sum of all the later rewards."
"The value of a state depends on what you do."
"The expected next reward and expected value of the next state naturally leads to the notion of an error."
"The TD error is what we're going to use to replace the normal conventional error."
"We're going to use the reward plus gamma times the next return essentially as a target."
"The value of a state is moved towards the expectation of the first reward and the expectation of gamma times the value of the next state."
"Reinforcement learning is a very special type of machine learning algorithm that tries to teach by setting some desired outcome."
"What gets me so excited about DPRL is agents that can take in what's on the screen, visual inputs, and actually you could run RL on different games and it would learn to play different games."
"With the help of reinforcement learning, it was possible for a computer to beat the best human player, the human world champion at Go."
"The only thing you program is a reinforcement learning algorithm, and then the reinforcement learning algorithm is what essentially trains the robot and allows it to acquire these skills."
"Model-based reinforcement learning is the setting where you don't have a model but you're going to learn it from experience."
"This event is designed to equip you with all the skills and knowledge required to excel in reinforcement learning applications and effectively leverage human input to enhance AI systems."
"Reinforcement learning works completely different. It allows a computer to take a decision based on past rewards for its actions."
"Reinforcement learning deals with agents acting on an environment, causing some change in that environment and receiving a reward in the process."
"Solving the reinforcement learning problem then becomes an issue of constructing a policy that allows the agent to seek out the most profitable states."
"PPO is a deep reinforcement learning algorithm proposed by OpenAI in 2017 and it has since become one of the most popular reinforcement learning algorithms."
"To read more on RL and PPO, I highly recommend checking out Joshua Achiam's OpenAI Spinning Up."
"REINFORCE is one of the most common reinforcement learning policy gradient algorithms."
"You can make reinforcement learning robust so that you can actually start to train in a simulated environment and then deploy it in the real world environment."
"Reinforcement learning agents learn to cooperate with other artificial intelligence agents and, more importantly, humans."
"What's special about reinforcement learning compared to supervised learning is that in practice we end up with this non-stationary sequence of value functions that we're trying to estimate."
"The whole point of reinforcement learning is there's no supervisor to tell us hey the right answer was 7.3, we need to figure that out directly from experience."
"We need to have some kind of exploration."
"By the end of today's class, hopefully you'll be able to go off into the world and actually program interesting reinforcement learning agents that can solve problems."
"Reinforcement learning is derived from the idea that you have an agent, you have a state, you have a policy."
"The purpose of the replay buffer is to store the state action reward new state and terminal flag transitions in the agent's memory."
"The target value for each state in our batch for the action we actually took is equal to the reward for that time step plus gamma times the value of the next step."
"We use reinforcement learning from human feedback to essentially build an in-between model."
"The main concept of reinforcement learning is that we have some agent which takes action in an environment."
"The exploration-exploitation trade-off is one of the key challenges in the game of reinforcement learning."
"Reinforcement learning is actually the hottest field in artificial intelligence these days."
"The environment tells the agent what's going on, the agent does something which is the action, then the agent gets a reward."
"Reinforcement learning allows this whole thing to unroll over time."
"Reinforcement learning is fundamentally much more challenging than any kind of supervised learning approach."
"One reason you might be excited about learning about this is the many success stories that exist in deep reinforcement learning."
"The objective would be to maximize expected sum of rewards under a policy π."
"The optimal value function V* is how much expected discounted reward you can get from state s if you use the best possible policy."
"The equation for the value function is called the value update or Bellman update or Bellman backup."
"In reinforcement learning, we assume we don't know what is the right answer; we need to figure out ourselves."
"We're doing both these parts of the course, one part is focusing on deep learning and the other part is focusing on reinforced learning."
"Many of you will know and have known before this course started that there is such a thing as deep reinforcement learning."
"We want to use reinforcement learning to solve large problems."
"The advantages tell us the reward that followed taking a particular action at a given time step."
"This algorithm is called reinforce with baselines."
"It's really a very simple algorithm to kind of get started in deep reinforcement learning."
"Why should we care about this particular flavor, why this problem of unknown MDP reinforcement learning?"
"The objective of RL is to come up with a policy that is hopefully close to optimal behavior."
"If our AI and our reinforcement learning model is able to do that, then we've completed the task successfully."
"In each of these cases, we can think of them as being reinforcement learning problems because we'd have some sort of agent like our computer, that is making decisions as it interacts with a person and it's trying to optimize some reward."
"We would love to have reinforcement learning algorithms that are both computationally efficient and sample efficient."
"OpenAI Gym is a very powerful library for simulating and visualizing the performance of reinforcement learning algorithms."
"By the end of this course, you will have a very strong understanding of reinforcement learning and open air gymnasium."
"That sort of gives you an idea as to how you can actually go and build your very own reinforcement learning model for just about any game."
"Hopefully you enjoyed this reinforcement learning tutorial because it is quite a powerful skill."
"We're going to try to build a reinforcement learning agent to play Street Fighter."
"Reinforcement learning is definitely a very exciting topic in terms of robotics."
"Ideally, if you see it increasing over time, you're seeing that your reinforcement learning model is learning to play better in that particular environment."
"Once you train the model with reinforcement learning from human feedback, it becomes much better at following instructions than the base GPT model."
"Any kind of task where you have some kind of goal that you want to achieve can be stated in terms of reinforcement learning."
"Reinforcement learning has been used in a lot of practical applications, for example, inventory management."
"One of the key contributions of reinforcement learning as we study it in AI is that it has really bridged this silo."
"Reinforcement learning algorithms are challenging to implement correctly, good results typically only come after fixing many seemingly trivial bugs."
"In the second half of today, we'll start on the final of the four major topics of the class, which is reinforcement learning."
"The output of most reinforcement learning algorithms will be a policy, or controller, that maps from states to actions."
"The goal of reinforcement learning is to choose actions over time, to maximize the expected total payoff."
"Reinforcement learning is the coolest thing ever."
"There's just something that's still magical about reinforcement learning that gets me excited every single time."
"The objective of reinforcement learning is for the agent to maximize its rewards inside of the environment."
"The agent gives the environment actions, and the environment returns states and rewards."
"A policy is a function that takes in a state and returns an action."
"The value of taking action A in state S is equal to the reward you get in the next time step plus the value of taking the best action in the next state S Prime, multiplied by a discount factor."
"The goal isn't just about seeking immediate gratification; instead, the agent aims to maximize its rewards over the long run."
"We're using reinforcement learning to train the computer to play Super Mario Bros."
"The most exciting thing about deep reinforcement learning is that it's the right framework for studying artificial intelligence."
"Reinforcement learning gives us the right problem statement which is how do we learn in sequential decision making tasks."
"The main idea of reinforcement is that here the tasks roughly speaking about learning to make sequential decisions."
"RLHF is a fine-tuning approach that aligns our outputs with human desires."
"The next section will delve into what are the challenges of combining reinforcement learning with deep learning."
"The problem in RL is to behave in such a way so that you get as much reward as possible in the long run."
"In TD learning or Q learning, we use a target to bootstrap."
"That's always gonna be equal to the immediate reward plus gamma times the sum over the next states, value of S prime."
"Real world RL now is much more practical than it's ever been before."
"Reinforcement learning is about learning to achieve goals."
"When you train a dog, the dog is the reinforcement learning agent and you as a human provide rewards."
"The goal in reinforcement learning is for this agent to figure out through its own trial and error how to get high reward."
"So to conclude reinforcement learning is helpful when we monitor a model to do something, beyond just mimicking the way humans perform a task."
"Reinforcement learning is naturally designed to plan a sequence of actions to maximize long-term rewards."
"If we can succeed at this, then we could not only allow current reinforcement learning techniques to generalize more widely, but we could also imagine applying reinforcement learning to domains where currently it's considered to be infeasible."
"In reinforcement learning, an agent receives a reward to evaluate its previous action."
"...the general value function is in a sense very much like a standard value function in that it is a prediction about the expected cumulative discounted sum of a suitable scalar signal under a given policy."
"In reinforcement learning, we do not have access to a full dataset and the scale of prediction is even known stationary, so it even changes over time."
"Instead of changing the cumulative or the discount or the target policy that we're making predictions about, it changes the type of prediction that we make."
"The reinforcement learner can automatically learn to do that very intuitive like I mentioned before."
"Reinforcement learners naturally incorporate elements of exploration and knowledge gathering."
"Some of the most exciting applications of reinforcement learning coming down the pipe I think will be robotics."
"Reinforcement learning today is used in a growing number of robotics applications."
"There is fascinating work using reinforcement learning for optimizing entire factory deployments."
"In reinforcement learning, we care about long-term value, not only immediate reward."
"Can we actually do a reinforcement learning to be able to learn a policy that will actually be able to keep this garden under control?"
"We use an optimistic model, the consequence that we are following the kind of standard reinforcement learning paradigm of being optimistic in the face of uncertainty."
"Reinforcement learning originally was inspired by behavioral psychology."
"This is CS 6700 Reinforcement Learning."
"Reinforcement learning in natural systems is about learning how to behave from a reward structure."
"There is really a very interesting bridge in reinforcement learning between the computational side and the neuroscience and cognitive science side of things."
"Human feedback reinforced learning... it's basically you go thumbs up or thumbs down and you tell it okay this is good or this is bad."
"Reinforcement learning is an approach which involves trial and error, learning from the actions and their consequences."
"In reinforcement learning, you only know something is bad once you've experienced it."
"Reinforcement learning is essentially like Markov decision processes."
"Offline RL is quite difficult but has enormous promise, and initial results suggest that it can be extremely powerful."
"RL as probabilistic inference... is the task of learning a policy such that we get optimal behavior."
"We can do the same thing in RL, in reinforcement learning."
"Temporal differences provide us with a method of calculating how much the Q value for the action taken in the previous state should be changed."
"After every single action the AI takes, we're going to give it a reward, telling it how well it's doing."
"This is an incredible achievement for reinforcement learning; they captivated our imagination of what's possible."
"Reinforcement learning with human feedback will really shine in this next era."
"There is a sense in which I agree, which is we shouldn't believe that with either RL or supervised learning we basically have all the tools we need, and then it's just a matter of finding new architectures to make progress towards AI."