Tags Categories Archive. Office Hours : MWby sign-up only, room TBD Communication : Piazza will be used for announcements, general questions and discussions, clarifications about assignments, student questions to each other, and so on.

The course is now full, and enrollment has closed. For people who are not enrolled, but interested in following and discussing the course, there is a subreddit forum here: reddit. This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning. Students who are not familiar with the concepts below are encouraged to brush up using the references provided right below this list.

Below you can find an outline of the course. Slides and references will be posted as the course proceeds. The course may be recorded this year. John also gave a lecture series at MLSS, and videos are available:. An abbreviated version of this course was offered in Fall Home Tags Categories Archive.

Lecture Videos The course may be recorded this year.Here presents an example with known linear dynamics and linear quadratic cost i. Note may contain quadraic terms of its elements, velocity and acceleration for example. Solve symbols backwards from last action to first statefill values forwards from first action to last state.

Assume all forces are active contact set is constantbut apply high penalties for use of forces where. Errors in feed-forward results will accumulate, getting the model into states that are off the labeled trajectory.

Simulate expert policies at states that are slightly off, by generating a correction term pointing towards the opposite direction of the error. Discounted problem can be obtained by adding transitions to sink statewhere agent gets stuck and recieves zero reward.

Then substitute with whatever you want, saywhere is substituted with the whole trajectorywe get. But small differences determine policy, therefore they could be easily submerged by the swing in! Note Luminance channel is much more important! Consider image compressing with jpeg. Using intermediate gives an intermediate amount of bias and variance.

For code, see GitHub Repo. Note View error from a stocastic perspective: the higher an error is, the more the mean is deviated from the most probable location in the space if you set label as MPL or mean roughlyi.

In short. The rest will be basic RTFM. Goal: Solve or expanded as Here presents an example with known linear dynamics and linear quadratic cost i.

### Implementing Deep Reinforcement Learning Models with Tensorflow + OpenAI Gym

Linear Quadratic Cost Methodology Solve symbols backwards from last action to first statefill values forwards from first action to last state. Model-Based RL v1. Model Representation Dynamics equation Inverse dynamics function Inverse dynamics residual Likelihood of being on the trajectory. Solution: Noisy Training Data Simulate expert policies at states that are slightly off, by generating a correction term pointing towards the opposite direction of the error. Input: Output: Decompose Policy Optimization Problem Trajectory optimization Stay close to policy Regression Movement with Model Uncertianty Generate noisy models varying: limb mass limb conter of mass contact locations etc.

Problem Swap maxes and exprectations Solve innermost problem Substitude original problem recurrently given Discounted Setting Dealing with infinite, or large, timestep. Discount factordownweights future rewards Discounted return: Effective time horizon Discounted problem can be obtained by adding transitions to sink statewhere agent gets stuck and recieves zero reward Infinite-Horizon V.

Via Finite-Horizon V. Pretend there exists finite horizonignore Error Resulting nonstationary policy only suboptimal by converges to optimal policy as Infinite-Horizon V. Via Operator View : is a vector with number of dimensions equal to the degree of freedomness of state V. Discrete action space classification : network outputs vector of probabilities Continuous action space regression : network outputs mean and diagonal covariance of Gaussian Prolicy Gradient Methods: Overview Goal Intuitions Collect a bunch of trajectories, and… Make the good trajectories more probable Make the good actions more probable Push the actions towards good actions Score Function Gradient Estimator Have expectationwant gradient w.

Hack Switch place for and. Now we have the gradient Then substitute with whatever you want, saywhere is substituted with the whole trajectorywe get Then we look at. Introduce Baseline Further reduce variance by introducing baseline. Near optimal choice is expected return, Discounts for Variance Reduction Introduce discount factorwhich ignores delayed effects between actions and rewards.

Solution Parameterize Q function as follows Prioritized Replay Bellman error loss: Can use importance sampling to favor timestep with large gradient, allowing faster backwards propagation of reward info Use last Bellman errorwhere as proxy for size of gradient Practical Tips User Huber loss on Bellman error Use Double DQN Try your own skills at navigating the environment based on processed frames, in order to test out your data preprocessing e.

Neural Methods Pros: good results Cons: no degree of control Color Channel Luminance channel texture and line info Color channel color info Note Luminance channel is much more important! The reward function is unclear.Do you want to know more about it?

This is the right opportunity for you to finally learn Deep RL and use it on new and exciting projects and applications.

The ultimate aim is to use these general-purpose technologies and apply them to all sorts of important real world problems. Demis Hassabis. Stay tuned and follow me on and 60DaysRLChallenge. Now we have also a Slack channel. To get an invitation, email me at andrea. Also, email me if you have any idea, suggestion or improvement. Those who cannot remember the past are condemned to repeat it - George Santayana.

This week, we will learn about the basic blocks of reinforcement learning, starting from the definition of the problem all the way through the estimation and optimization of the functions that are used to express the quality of a policy or state. In the former case, only few changes are needed. Play with them, and if you feel confident, you can implement Prioritized replay, Dueling networks or Distributional RL.

### A Free course in Deep Reinforcement Learning from beginner to expert.

To know more about these improvements read the papers! Week 4 introduce Policy Gradient methods, a class of algorithms that optimize directly the policy. These algorithms combine both policy gradient the actor and value function the critic. Vanilla PG and A2C applied to CartPole - The exercise of this week is to implement a policy gradient method or a more sophisticated actor-critic.

In the repository you can find an implemented version of PG and A2C. Bug Alert! Pay attention that A2C give me strange result. Furthermore, in the folder you can find other resources that will help you in the development of the project. Have fun! They are derivate-free black-box algorithms that require more data than RL to learn but are able to scale up across thousands of CPUs.

You can modify it to play more difficult environments or add your ideas. The algorithms studied up to now are model-free, meaning that they only choose the better action given a state. These algorithms achieve very good performance but require a lot of training data.The recent success of AI has been in large part due in part to advances in hardware and software systems.

These systems have enabled training increasingly complex models on ever larger datasets. In the process, these systems have also simplified model development, enabling the rapid growth in the machine learning community. These new hardware and software systems include a new generation of GPUs and hardware accelerators e. In this course, we will describe the latest trends in systems designs to better support the next generation of AI applications, and applications of AI to optimize the architecture and the performance of systems.

The format of this course will be a mix of lectures, seminar-style discussions, and student presentations. Students will be responsible for paper readings, and completing a hands-on project. Readings will be selected from recent conference proceedings and journals.

For projects, we will strongly encourage teams that contains both AI and systems students. This is a tentative schedule. Specific readings are subject to change as new material is published.

This lecture will be an overview of the class, requirements, and an introduction to what makes great AI-Systems research. Reading notes for the two required readings below must be submitted using this google form by Monday the 28th at AM.

We have asked that for each reading you answer the following questions:. If you find some of the reading confusing and want a more gentle introduction, the optional reading contains some useful explanatory blog posts that may help. Detailed candidate project descriptions will be posted shortly. However, students are encourage to find projects that relate to their ongoing research. Grades will be largely based on class participation and projects.

In addition, we will require weekly paper summaries submitted before class. Gonzalez Announcements : Piazza Sign-up to Present : Google Spreadsheet Project Ideas : Google Spreadsheet If you have reading suggestions please send a pull request to this course website on Github by modifying the index. Course Description The recent success of AI has been in large part due in part to advances in hardware and software systems.

Course Syllabus This is a tentative schedule. Jump to Today Week Date Lec. We have asked that for each reading you answer the following questions: What is the problem that is being solved?

What are the metrics of success?In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wild. The implementation is gonna be built in Tensorflow and OpenAI gym environment. It makes life so much easier when you have multiple projects with conflicting requirements; i. For a minimal installation, run:.

If you are interested in playing with Atari games or other advanced packages, please continue to get a couple of system packages installed. For Atari, go to the gym directory and pip install it. This post is pretty helpful if you have troubles with ALE arcade learning environment installation.

The OpenAI Gym toolkit provides a set of physical simulation environments, games, and robot simulators that we can play with and design reinforcement learning agents for.

**Deep Reinforcement Learning and Imitation Learning - MuJoCo Humanoid-v2**

An environment object can be initialized by gym. The formats of action and observation of an environment are defined by env. The key point is while estimating what is the next action, it does not follow the current policy but rather adopt the best Q value the part in red independently.

In a naive implementation, the Q value for all s, a pairs can be simply tracked in a dict.

## CS 294: Deep Reinforcement Learning（8）

No complicated machine learning model is involved yet. Most gym environments have a multi-dimensional continuous observation space gym. To make sure our Q dictionary will not explode by trying to memorize an infinite number of keys, we apply a wrapper to discretize the observation.

The concept of wrappers is very powerful, with which we are capable to customize observation, action, step function, etc. No matter how many wrappers are applied, env. The full code of QLearningPolicy is available here. Deep Q-network is a seminal piece of work to make the training of Q-learning more stable and more data-efficient, when the Q value is approximated with a nonlinear function.

Two key ingredients are experience replay and a separately updated target network. The Q network can be a multi-layer dense neural network, a convolutional network, or a recurrent network, depending on the problem. The input tensors are:. We have two networks of the same structure. Both have the same network architectures with the state observation as the inputs and Q values over all the actions as the outputs. Note that tf. This two-step reinforcing procedure could potentially lead to overestimation of an already overestimated value, further leading to training instability.

The solution proposed by double Q-learning Hasselt, is to decouple the action selection and action value estimation by using two Q networks, and : when is being updated, decides the best next action, and vice versa. In the code, we add a new tensor for getting the action selected by the primary Q network as the input and a tensor operation for selecting this action.

Here I used tf.The course lectures are available below. The course is not being offered as an online course, and the videos are provided only for your personal informational and entertainment purposes.

They are not part of any course requirement or degree-bearing university program. For all videos, click here. For live stream, click here. Below you can find an outline of the course. Slides and references will be posted as the course proceeds.

CS or equivalent is a prerequisite for the course. This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning. Students who are not familiar with the concepts below are encouraged to brush up using the references provided right below this list. CS Deep Reinforcement Learning, Spring If you are a UC Berkeley undergraduate student looking to enroll in the fall offering of this course: We will post a form that you may fill out to provide us with some information about your background during the summer.

Please do not email the instructors about enrollment: the form will be used to collect all information we need. Office Hours : MWby appointment see signup sheet on Piazza Communication : Piazza will be used for announcements, general questions and discussions, clarifications about assignments, student questions to each other, and so on.

For people who are not enrolled, but interested in following and discussing the course, there is a subreddit forum here: reddit. Unfortunately, we do not have any license that we can provide to students who are not officially enrolled in the course for credit.

Lectures, Readings, and Assignments Below you can find an outline of the course. If you are a UC Berkeley undergraduate student looking to enroll in the fall offering of this course: We will post a form that you may fill out to provide us with some information about your background during the summer.

Communication : Piazza will be used for announcements, general questions and discussions, clarifications about assignments, student questions to each other, and so on. Please do not email the course instructors about MuJoCo licenses if you are not enrolled in the course.Course announcements will be announced through Piazza. If you are in the class, sign up on Piazza. For more information about deep learning at Berkeley, sign up for the talk announcement mailing list.

In recent years, deep learning has enabled huge progress in many domains including computer vision, speech, NLP, and robotics. It has become the leading solution for many tasks, from winning the ImageNet competition to winning at Go against a world champion. This class is designed to help students develop a deeper understanding of deep learning and explore new research directions and applications of deep learning. It assumes that students already have a basic understanding of deep learning.

In particular, we will explore a selected list of new, cutting-edge topics in deep learning, including new techniques and architectures in deep learning, security and privacy issues in deep learning, recent advances in the theoretical and systems aspects of deep learning, and new application domains of deep learning such as autonomous driving.

This is a lecture, discussion, and project oriented class. Each lecture will focus on one of the topics, including a survey of the state-of-the-art in the area and an in-depth discussion of the topic. Each week, students are expected to complete reading assignments before class and participate actively in class discussion.

Students will also form project groups two to three people per group and complete a research-quality class project. For undergraduates : Please note that this is a graduate-level class. If you are an undergraduate student and would like to enroll in the class, please fill out this form and come to the first lecture of the class.

Qualified undergraduates will be given instructor codes to be allowed to register for the class after the first lecture of the class, subject to space availability. If you have not received grades for some classes that you are currently enrolled in, please choose Currently Enrolled and then update the form when you receive your final grades.

You may also be interested in this classwhich is open to undergraduates. Stefano Soatto. The Emergence Theory of Deep Learning. Alison Gopnik. Main Reading: Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory Background Reading: When Younger Learners Can Be Better or at Least More Open-Minded Than Older Ones Probabilistic models, learning algorithms, and response variability: sampling in cognitive development.

Mike Lewis. Main Reading: Deal or No Deal? Deal or No Deal? End-to-End Learning for Negotiation Dialogues.