Congratulations to Pieter Abbeel who has been awarded the ACM Prize in Computing for his contribution to robot learning, including learning from demonstrations and deep reinforcement learning for robotic control. Pieter's research has covered the following: Pieter Abbeel is a Professor of Computer Science and Electrical Engineering at the University of California, Berkeley and the Co-Founder, President and Chief Scientist at Covariant, an AI robotics company. He also hosts the The Robot Brains podcast. The ACM Prize in Computing recognizes an early- to mid-career fundamental, innovative contribution in computing that, through its depth, impact and broad implications, exemplifies the greatest achievements in the discipline. The award carries a prize of $250,000.

This year's ACM Prize in Computing is going toward a machine learning specialist whose work, even if you haven't heard of him, is likely to be familiar. Pieter Abbeel, UC Berkeley professor and co-founder of AI robotics company Covariant, was awarded the prize and its $250,000 bounty, which is given to those in the machine learning field "whose research contributions have fundamental impact and broad implications." Abbeel is a professor of computer science and electrical engineering whose work has already received some recognition. Along with this new award, he was named a top young innovator under 25 by the MIT Technology Review and won a prize given out to the best US PhD thesis in robotics and automation. ACM said Abbeel was a trailblazer in apprenticeship and reinforcement learning, and highlighted a clothes-folding robot he designed that was better able to manipulate deformable objects.

Recently, researchers from OpenAI published a new paper that proposes a method to address this challenge by creating RL models that know what it means to make progress on a new task, by having experienced making progress on similar tasks in the past. Titled Evolved Policy Gradients(EPG), the OpenAI research paper introduces new meta-learning technique based on the concept of a loss function that qualifies the learning progress. When used in RL models, the EPG method does not encode the knowledge explicitly through memorized behaviors but, instead, it uses an implicitly mechanism through a learned loss function. The EPG end goal is that RL agents that can use this loss function to learn a novel task. In initial tests, EPG seems to improves on standard RL algorithms by allowing the loss function to be adaptive to the environment and agent history, leading to faster learning and the potential for learning without external rewards.

$Q$-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of $Q$-learning requires approximation of the state-action value function (also known as the $Q$-function). In this work, we provide online random forests as $Q$-function approximators and propose a novel method wherein the random forest is grown as learning proceeds (through expanding forests). We demonstrate improved performance of our methods over state-of-the-art Deep $Q$-Networks in two OpenAI gyms (`blackjack' and `inverted pendulum') but not in the `lunar lander' gym. We suspect that the resilience to overfitting enjoyed by random forests recommends our method for common tasks that do not require a strong representation of the problem domain. We show that expanding forests (in which the number of trees increases as data comes in) improve performance, suggesting that expanding forests are viable for other applications of online random forests beyond the reinforcement learning setting.

Reinforcement learning is an area of machine learning where an agent (e.g., a computer program) seeks to progress towards a reward (e.g., winning a …

This Asset we are sharing with you the Advanced Reinforcement Learning in Python: cutting-edge DQNs free download links. This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents capable of solving decision-making tasks.

Reinforcement learning (RL) provides a theoretical framework for continuously improving an agent's behavior via trial and error. However, efficiently learning policies from scratch can be very difficult, particularly for tasks with exploration challenges. In such settings, it might be desirable to initialize RL with an existing policy, offline data, or demonstrations. However, naively performing such initialization in RL often works poorly, especially for value-based methods. In this paper, we present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy, and is compatible with any RL approach. In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks: a guide-policy, and an exploration-policy. By using the guide-policy to form a curriculum of starting states for the exploration-policy, we are able to efficiently improve performance on a set of simulated robotic tasks. We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms, particularly in the small-data regime. In addition, we provide an upper bound on the sample complexity of JSRL and show that with the help of a guide-policy, one can improve the sample complexity for non-optimism exploration methods from exponential in horizon to polynomial.

We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. We stress the constraint of environment heterogeneity, which means $n$ environments corresponding to these $n$ agents have different state transitions. To obtain a value function or a policy function which optimizes the overall performance in all environments, we propose two federated RL algorithms, \texttt{QAvg} and \texttt{PAvg}. We theoretically prove that these algorithms converge to suboptimal solutions, while such suboptimality depends on how heterogeneous these $n$ environments are. Moreover, we propose a heuristic that achieves personalization by embedding the $n$ environments into $n$ vectors. The personalization heuristic not only improves the training but also allows for better generalization to new environments.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This article is written by Berend Gort & Bruce Yang, core team members of the Open-Source project AI4Finance. This project is an open-source community sharing AI tools for finance, and a part of the Columbia University in New York. Our previous article described the Combinatorial PurgedKFold Cross-Validation method in detail for classifiers (or regressors) with regular predictions.

The fundamental ingredient of our approach is a control policy stabilization around desired configuration paths (time-reparameterized trajectories) that can be rigorously The past decade has seen successful applications of justified in the high gain limit (HGL). We call deep neural networks (NN) to various machine learning this approach Configuration Path Control (CPC). It has tasks, such as image classification [18, 20], speech some overlap with the zero dynamics (ZD) concept [3], recognition [12] and language translation [32, 37]. In the which is central to the Hybrid Zero Dynamics (HZD) field of reinforcement learning (RL), the employment of framework in the context of bipedal walkers [9, 36]. We deep NNs as expressive function approximators has been reinterpret the derived CPC control law in the language crucial in tackling many difficult problems involving an of the HZD literature, by re-stating it in terms of the agent interacting with its environment with the goal of reparameterization invariant virtual constraints.