"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
It feels as though 2019 has gone by in a flash, that said, it has been a year in which we have seen great advancement in AI application methods and technical discovery, paving the way for future development. We are incredibly grateful to have had the leading minds in AI & Deep Learning present their latest work at our summits in San Francisco, Boston, Montreal and more, so we thought we would share thirty of our highlight videos with you as we think everybody needs to see them!. We were delighted to be joined by Dawn at the Deep Reinforcement Learning Summit in June of 2019, presenting the latest industry research on Secure Deep Reinforcement Learning, covering both the lessons leant in the lead up to her presentation, current challenges faced for advancement, and the future direction of which her research is set to take. You can see Dawn's full presentation from June here. Reinforcement Learning is somewhat of a hotbed for research, this year alone we have seen several presentations that have broken down the ins and outs of RL, that said, Doina's talk just last month gave us some new angles on the latest algorithmic development.
Reinforcement learning (RL) continues to be less valuable for business applications than supervised learning, and even unsupervised learning. It is successfully applied only in areas where huge amounts of simulated data can be generated, like robotics and games. However, many experts recognize RL as a promising path towards Artificial General Intelligence (AGI), or true intelligence. Thus, research teams from top institutions and tech leaders are seeking ways to make RL algorithms more sample-efficient and stable. We've selected and summarized 10 research papers that we think are representative of the latest research trends in reinforcement learning. The papers explore, among others, the interaction of multiple agents, off-policy learning, and more efficient exploration.
I definitely understand that, though I am a bit surprised to see MSR papers being referenced often in an RL workshop. I don't quite remember any RL papers of theirs that weren't strongly theoretical (other than that HRL paper by Hoang et Daume). I'm also a PhD student that until recently was doing RL, but have become tired of working in a field that's moving this fast and seems to be taking a'fast science' approach. It feels almost impossible to have any sort of real impact. I'm also just bored with reading RL papers, about 10% of the time I spend reading them is understanding the novelty, and the rest studying their experimentation to get a feel for whether it's worth anything.
Sign in to report inappropriate content. Robot being trained for 500 iterations to learn to control inclination of torso. This is done by selecting goal inclinations the robot attempts to reach, while creating a network of postures to move between. In the final evaluation (0:49), goals are selected manually, to force the robot to roll around 360 degrees to reach them.
In this blog, I'll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. In doing so, I'll demonstrate the application of ML techniques to the manufacturing industry and the role of the Machine Learning Product Manager. Machine learning (ML) has had an incredible impact across industries with numerous applications such as personalized TV recommendations and dynamic price models in your rideshare app. Because it is such a core component to the success of companies in the tech industry, advances in ML research and applications are developing at an astonishing rate. For industries outside of tech, ML can be utilized to personalize a user's experience, automate laborious tasks and optimize subjective decision making.
In previous posts (here and here) I introduced Double Q learning and the Dueling Q architecture. These followed on from posts about deep Q learning, and showed how double Q and dueling Q learning is superior to vanilla deep Q learning. However, these posts only included examples of simplistic environments like the OpenAI Cartpole environment. These types of environments are good to learn on, but more complicated environments are both more interesting and fun. They also demonstrate better the complexities of implementing deep reinforcement learning in realistic cases.
Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, reinforcement learning (RL) can acquire behaviors from low-level inputs directly, but struggles with temporally extended tasks. Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches? We show that goal-conditioned policies learned with RL can be incorporated into planning, such that a planner can focus on which states to reach, rather than how those states are reached.