In recent months, researchers at OpenAI have been focusing on developing artificial intelligence (AI) that learns better. Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines. Now, a new algorithm lets their AI learn from its own mistakes, almost as human beings do. The development comes from a new open-source algorithm called Hindsight Experience Replay (HER), which OpenAI researchers released earlier this week. As its name suggests, HER helps an AI agent "look back" in hindsight, so to speak, as it completes a task.

The Shadow Robot Company, that manufactures robotic hands for grasping and manipulation for real world challenges from fruit picking to bomb disposal, is supplying its Shadow Dexterous Hands to OpenAI, a non-profit company focusing on the path to safe artificial intelligence. The research is claimed to have created eight newly released environments, four of which using the Shadow Hand robot to solve realistic manipulation tasks. The Shadow Hand is tactile enough to rotate a block and a solid egg and flexible enough to move a pen between its fingers. Each task has a'goal', such as achieving the desired orientation of a block in the Shadow hand block manipulation task. Along with releasing these new robotics environments, OpenAI is releasing code for Hindsight Experience Replay, a reinforcement learning algorithm that can teach and improve robotic technology to learn from failure.

It asks to use an LSTM and predict the parity of bit sequences of length 50. Naive attempts didn't work (large hidden state, different rnn cells, different optimization algos, etc). It was a fun challenge. I like that it's very easy to state but requires a bit of insight to figure out. Do you know of any others like that?

In recent months, researchers at OpenAI have been focusing on developing artificial intelligence (AI) that learns better. Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines. Now, a new algorithm lets their AI learn from its own mistakes, almost as human beings do. The development comes from a new open-source algorithm called Hindsight Experience Replay (HER), which OpenAI researchers released earlier this week. As its name suggests, HER helps an AI agent "look back" in hindsight, so to speak, as it completes a task.

Researchers at Elon Musk's startup, OpenAI, think they have discovered the most efficient way to train artificial neural networks: have them compete against each other. For more videos, subscribe to Mashable News: http://on.mash.to/SubscribeNews Give us a follow: Facebook: https://www.facebook.com/mashable/

Mujoco is mostly a physics engine, and I'm willing to bet that whatever parts you're thinking of when you say it's "more" than a physics engine either exist in some form in Bullet and the rest, or aren't relevant for RL. The things you listed are engines that delegate to other projects for their physics simulation, and come with a ton of heavyweight baggage that you don't need to do RL.

This release includes four environments using the Fetch research platform and four environments using the ShadowHand robot. The manipulation tasks contained in these environments are significantly more difficult than the MuJoCo continuous control environments currently available in Gym, all of which are now easily solvable using recently released algorithms like PPO. Furthermore, our newly released environments use models of real robots and require the agent to solve realistic tasks. FetchReach-v0: Fetch has to move its end-effector to the desired goal position. FetchSlide-v0: Fetch has to hit a puck across a long table such that it slides and comes to rest on the desired goal.

OpenAI today updated Gym – its system for training intelligent software – so that developers can teach physical robots to hold pens, pick up and move objects, and so on. Gym was launched in 2016, and is a toolkit for teaching programs new tricks, such as playing Atari games and balancing poles, via reinforcement learning (RL). Now, OpenAI has added a bunch of simulated environments designed to train physical robots how to move and interact with things around them albeit in a virtual world. For example, the simulated environments can be used to teach robotic fingers to play an instrument, or pick and lift an object from the table. This is useful for folks interested in rapidly training intelligent robots over thousands of exercises, without having to rig up a relatively slow-moving physical bot, or before they have a chance to get hold of the hardware.

Being able to learn from mistakes is a powerful ability that humans (being mistake-prone) take advantage of all the time. Even if we screw something up that we're trying to do, we probably got parts of it at least a little bit correct, and we can build off of the things that we did not to do better next time. Robots can use similar trial-and-error techniques to learn new tasks. With reinforcement learning, a robot tries different ways of doing a thing, and gets rewarded whenever an attempt helps it to get closer to the goal. Based on the reinforcement provided by that reward, the robot tries more of those same sorts of things until it succeeds. Where humans differ is in how we're able to learn from our failures as well as our successes.

I'm a High School student with a reasonably basic research project where I am to implement an AI Agent to learn and master games and graph a linear regression of its time to mastery versus the task complexity. My partner and I have decided task complexity is to be based on the number of state spaces (or different inputs) the AI can use. We would like to find a good primary AI and have been using public OpenAi templates. Do any of you guys have suggestions on an efficient and effective way to make a "cookie cutter" algorithm? We'd like for it to be as easy to understand as possible.