First, if you don't have the background about deep reinforcement learning, you can think of it as major algorithm behind AlphaGo. Therefore, authors provide asynchronous Methods for Deep Reinforcement Learning to overcome these drawbacks. Using CPU instead of GPU, we can open multi thread to run the same environment but share the same model weight. After reading the pseudocode, we find that there is little difference from original DQN algorithm. The special point is the line -- t mod Iasyncupdate.

We made a tool that you can use. Reinforcement learning is much discussed these days with successes like AlphaGo. Wouldn't it be great if Reinforcement Learning algorithms could easily be used to solve all reinforcement learning problems? But there is a well-known problem: It's very easy to create natural RL problems for which all standard RL algorithms (epsilon-greedy Q-learning, SARSA, etc…) fail catastrophically. That's a serious limitation which both inspires research and which I suspect many people need to learn the hard way.

Because we'll need something tasty to swill when our robot overlords finally come into their full artificial intelligence, a company in the UK is attempting to figure out if robots can help humans brew a better beer. While there won't be robots stirring batches of wort or sorting hops, artificial intelligence will play a big part in London-based firm IntelligentX's plan to brew beer, CNET reports. Here's how it'd work: consumers would try one of the company's four beers -- Amber AI, Black AI, Golden AI and Pale AI ---- and then weigh in via Facebook chat bot on the experience. That feedback will be fed to an algorithm called Automated Brewing Intelligence, or ABI, which will use the information to make changes to the next batch. Reinforcement learning and a process called bayesian decision making will teach the AI about the brewing experience.

Pieter Abbeel is an associate professor in UC Berkeley's EECS department, where he works in machine learning and robotics--in particular his research is on making robots learn from people (apprenticeship learning) and how to make robots learn through their own trial and error (reinforcement learning). Pieter's robots have learned advanced helicopter aerobatics, knot tying, basic assembly, and organizing laundry. He has won various awards, including best paper awards at ICML and ICRA, the Sloan Fellowship, the Air Force Office of Scientific Research Young Investigator Program (AFOSR-YIP) Award, the Office of Naval Research Young Investigator Program (ONR-YIP) Award, the DARPA Young Faculty Award (DARPA-YFA), the National Science Foundation Faculty Early Career Development Program Award (NSF-CAREER), the Presidential Early Career Award for Scientists and Engineers (PECASE), the CRA-E Undergraduate Research Faculty Mentoring Award, the MIT TR35, the IEEE Robotics and Automation Society (RAS) Early Career Award, and the Dick Volz Best US PhD Thesis in Robotics and Automation Award.

We consider the problem of reinforcement learning over episodes of a finite-horizon deterministic system and as a solution propose optimistic constraint propagation (OCP), an algorithm designed to synthesize efficient exploration and value function generalization. We establish that when the true value function lies within a given hypothesis class, OCP selects optimal actions over all but at most K episodes, where K is the eluder dimension of the given hypothesis class. We establish further efficiency and asymptotic performance guarantees that apply even if the true value function does not lie in the given hypothesis class, for the special case where the hypothesis class is the span of pre-specified indicator functions over disjoint sets. We also discuss the computational complexity of OCP and present computational results involving two illustrative examples.

Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as ɛ-greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not compatible with nonlinearly parameterized value functions. As a first step towards addressing such contexts we develop bootstrapped DQN. We demonstrate that bootstrapped DQN can combine deep exploration with deep neural networks for exponentially faster learning than any dithering strategy. In the Arcade Learning Environment bootstrapped DQN substantially improves learning speed and cumulative performance across most games.

The 2nd Indian Workshop on Machine Learning (iWML) will organized by the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), during July 1-3, 2016. This follows the inaugural edition of the workshop, held in 2013, which brought together several leading researchers and experts in machine learning and related areas from both the academia as well as the industry [link]. The second workshop seeks to further this effort and foster growth and excellence in the emerging machine learning community in India. We have significantly expanded the scope of the workshop, including talks and tutorials on modern and cutting edge topics such as reinforcement learning, non-convex optimization, deep learning, and contemporary applications of machine learning to medicine, social media, and vision. We hope attendees will benefit from the wide variety of topics being covered in the workshop, as well as the interaction with leading researchers.

Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still missing the tools to analayze their performance and visualize the temporal abstractions that they learn. In this paper, we present a novel method that automatically discovers an internal Semi Markov Decision Process (SMDP) model in the Deep Q Network's (DQN) learned representation. We suggest a novel visualization method that represents the SMDP model by a directed graph and visualize it above a t-SNE map. We show how can we interpret the agent's policy and give evidence for the hierarchical state aggregation that DQNs are learning automatically. Our algorithm is fully automatic, does not require any domain specific knowledge and is evaluated by a novel likelihood based evaluation criteria.

Hi folks, I have a few questions about NTM. Is there any extension to these models? There are extensions, most notably the Reinforcement Learning NTM which uses the Reinforce rule to apply hard attention to the memory, and also models that use the NTM with different access modules. There is an implementation in Torch.

In 2013, years before DeepMind would unveil an AI that would defeat of one of the world's best Go players, the company published an influential paper showing how "deep reinforcement learning" could be used to teach computers how to play Atari 2600 video games. While GPUs were originally designed as specialized processors optimized to render millions of pixels required for simulating 3D environments, repurposing GPUs to train artificial intelligence algorithms has been commonplace for a while. But it wasn't until I read Andrej Karpathy's recent post on reinforcement learning, however, that something clicked about how interesting this is: Graphics cards, originally designed for human vision of video games, are now being used for computer "vision" of video games. When I was growing up, getting a graphics card was kind of a Big Deal. The first one I got for Christmas in 1997 was a Pure3D Canopus Voodoo card based on the 3dfx chipset. It let me run Quake smoothly on my Pentium Compaq, which was a top priority of my life at the time.