Tag Archives: DeepMind

Introducing PlaNet: A Deep Planning Network for Reinforcement Learning



Research into how artificial agents can improve their decisions over time is progressing rapidly via reinforcement learning (RL). For this technique, an agent observes a stream of sensory inputs (e.g. camera images) while choosing actions (e.g. motor commands), and sometimes receives a reward for achieving a specified goal. Model-free approaches to RL aim to directly predict good actions from the sensory observations, enabling DeepMind's DQN to play Atari and other agents to control robots. However, this blackbox approach often requires several weeks of simulated interaction to learn through trial and error, limiting its usefulness in practice.

Model-based RL, in contrast, attempts to have agents learn how the world behaves in general. Instead of directly mapping observations to actions, this allows an agent to explicitly plan ahead, to more carefully select actions by "imagining" their long-term outcomes. Model-based approaches have achieved substantial successes, including AlphaGo, which imagines taking sequences of moves on a fictitious board with the known rules of the game. However, to leverage planning in unknown environments (such as controlling a robot given only pixels as input), the agent must learn the rules or dynamics from experience. Because such dynamics models in principle allow for higher efficiency and natural multi-task learning, creating models that are accurate enough for successful planning is a long-standing goal of RL.

To spur progress on this research challenge and in collaboration with DeepMind, we present the Deep Planning Network (PlaNet) agent, which learns a world model from image inputs only and successfully leverages it for planning. PlaNet solves a variety of image-based control tasks, competing with advanced model-free agents in terms of final performance while being 5000% more data efficient on average. We are additionally releasing the source code for the research community to build upon.
The PlaNet agent learning to solve a variety of continuous control tasks from images in 2000 attempts. Previous agents that do not learn a model of the environment often require 50 times as many attempts to reach comparable performance.
How PlaNet Works
In short, PlaNet learns a dynamics model given image inputs and efficiently plans with it to gather new experience. In contrast to previous methods that plan over images, we rely on a compact sequence of hidden or latent states. This is called a latent dynamics model: instead of directly predicting from one image to the next image, we predict the latent state forward. The image and reward at each step is then generated from the corresponding latent state. By compressing the images in this way, the agent can automatically learn more abstract representations, such as positions and velocities of objects, making it easier to predict forward without having to generate images along the way.
Learned Latent Dynamics Model: In a latent dynamics model, the information of the input images is integrated into the hidden states (green) using the encoder network (grey trapezoids). The hidden state is then projected forward in time to predict future images (blue trapezoids) and rewards (blue rectangle).
To learn an accurate latent dynamics model, we introduce:
  • A Recurrent State Space Model: A latent dynamics model with both deterministic and stochastic components, allowing to predict a variety of possible futures as needed for robust planning, while remembering information over many time steps. Our experiments indicate both components to be crucial for high planning performance.
  • A Latent Overshooting Objective: We generalize the standard training objective for latent dynamics models to train multi-step predictions, by enforcing consistency between one-step and multi-step predictions in latent space. This yields a fast and effective objective that improves long-term predictions and is compatible with any latent sequence model.
While predicting future images allows us teach the model, encoding and decoding images (trapezoids in the figure above) requires significant computation, which would slow down planning. However, planning in the compact latent state space is fast since we only need to predict future rewards, and not images, to evaluate an action sequence. For example, the agent can imagine how the position of a ball and its distance to the goal will change for certain actions, without having to visualize the scenario. This allows us to compare 10,000 imagined action sequences with a large batch size every time the agent chooses an action. We then execute the first action of the best sequence found and replan at the next step.
Planning in Latent Space: For planning, we encode past images (gray trapezoid) into the current hidden state (green). From there, we efficiently predict future rewards for multiple action sequences. Note how the expensive image decoder (blue trapezoid) from the previous figure is gone. We then execute the first action of the best sequence found (red box).
Compared to our preceding work on world models, PlaNet works without a policy network -- it chooses actions purely by planning, so it benefits from model improvements on the spot. For the technical details, check out our online research paper or the PDF version.

PlaNet vs. Model-Free Methods
We evaluate PlaNet on continuous control tasks. The agent is only given image observations and rewards. We consider tasks that pose a variety of different challenges:
  • A cartpole swing-up task, with a fixed camera, so the cart can move out of sight. The agent thus must absorb and remember information over multiple frames.
  • A finger spin task that requires predicting two separate objects, as well as the interactions between them.
  • A cheetah running task that includes contacts with the ground that are difficult to predict precisely, calling for a model that can predict multiple possible futures.
  • A cup task, which only provides a sparse reward signal once a ball is caught. This demands accurate predictions far into the future to plan a precise sequence of actions.
  • A walker task, in which a simulated robot starts off by lying on the ground, and must first learn to stand up and then walk.
PlaNet agents trained on a variety of image-based control tasks. The animation shows the input images as the agent is solving the tasks. The tasks pose different challenges: partial observability, contacts with the ground, sparse rewards for catching a ball, and controlling a challenging bipedal robot.
Our work constitutes one of the first examples where planning with a learned model outperforms model-free methods on image-based tasks. The table below compares PlaNet to the well-known A3C agent and the D4PG agent, that combines recent advances in model-free RL. The numbers for these baselines are taken from the DeepMind Control Suite. PlaNet clearly outperforms A3C on all tasks and reaches final performance close to D4PG while, using 5000% less interaction with the environment on average.
One Agent for All Tasks
Additionally, we train a single PlaNet agent to solve all six tasks. The agent is randomly placed into different environments without knowing the task, so it needs to infer the task from its image observations. Without changes to the hyper parameters, the multi-task agent achieves the same mean performance as individual agents. While learning slower on the cartpole tasks, it learns substantially faster and reaches a higher final performance on the challenging walker task that requires exploration.
Video predictions of the PlaNet agent trained on multiple tasks. Holdout episodes collected with the trained agent are shown above and open-loop agent hallucinations below. The agent observes the first 5 frames as context to infer the task and state and accurately predicts ahead for 50 steps given a sequence of actions.
Conclusion
Our results showcase the promise of learning dynamics models for building autonomous RL agents. We advocate for further research that focuses on learning accurate dynamics models on tasks of even higher difficulty, such as 3D environments and real-world robotics tasks. A possible ingredient for scaling up is the processing power of TPUs. We are excited about the possibilities that model-based reinforcement learning opens up, including multi-task learning, hierarchical planning and active exploration using uncertainty estimates.

Acknowledgements
This project is a collaboration with Timothy Lillicrap, Ian Fischer, Ruben Villegas, Honglak Lee, David Ha and James Davidson. We further thank everybody who commented on our paper draft and provided feedback at any point throughout the project.




Source: Google AI Blog


Curiosity and Procrastination in Reinforcement Learning



Reinforcement learning (RL) is one of the most actively pursued research techniques of machine learning, in which an artificial agent receives a positive reward when it does something right, and negative reward otherwise. This carrot-and-stick approach is simple and universal, and allowed DeepMind to teach the DQN algorithm to play vintage Atari games and AlphaGoZero to play the ancient game of Go. This is also how OpenAI taught its OpenAI-Five algorithm to play the modern video game Dota, and how Google taught robotic arms to grasp new objects. However, despite the successes of RL, there are many challenges to making it an effective technique.

Standard RL algorithms struggle with environments where feedback to the agent is sparse — crucially, such environments are common in the real world. As an example, imagine trying to learn how to find your favorite cheese in a large maze-like supermarket. You search and search but the cheese section is nowhere to be found. If at every step you receive no “carrot” and no “stick”, there’s no way to tell if you are headed in the right direction or not. In the absence of rewards, what is to stop you from wandering around in circles? Nothing, except perhaps your curiosity, which motivates you go into a product section that looks unfamiliar to you in pursuit of your sought-after cheese.

In “Episodic Curiosity through Reachability” — the result of a collaboration between the Google Brain team, DeepMind and ETH Zürich — we propose a novel episodic memory-based model of granting RL rewards, akin to curiosity, which leads to exploring the environment. Since we want the agent not only to explore the environment but also to solve the original task, we add a reward bonus provided by our model to the original sparse task reward. The combined reward is not sparse anymore which allows standard RL algorithms to learn from it. Thus, our curiosity method expands the set of tasks which are solvable with RL.
Episodic Curiosity through Reachability: Observations are added to memory, reward is computed based on how far the current observation is from the most similar observation in memory. The agent receives more reward for seeing observations which are not yet represented in memory.
The key idea of our method is to store the agent's observations of the environment in an episodic memory, while also rewarding the agent for reaching observations not yet represented in memory. Being “not in memory” is the definition of novelty in our method — seeking such observations means seeking the unfamiliar. Such a drive to seek the unfamiliar will lead the artificial agent to new locations, thus keeping it from wandering in circles and ultimately help it stumble on the goal. As we will discuss later, our formulation can save the agent from undesired behaviours which some other formulations are prone to. Much to our surprise, those behaviours bear some similarity to what a layperson would call “procrastination”.

Previous Curiosity Formulations
While there have been many attempts to formulate curiosity in the past[1][2][3][4], in this post we  focus on one natural and very popular approach: curiosity through prediction-based surprise, explored in the recent paper “Curiosity-driven Exploration by Self-supervised Prediction” (commonly referred to as the ICM method). To illustrate how surprise leads to curiosity, again consider our analogy of looking for cheese in a supermarket.
Illustration © Indira Pasko, used under CC BY-NC-ND 4.0 license.
As you wander throughout the market, you try to predict the future (“Now I’m in the meat section, so I think the section around the corner is the fish section — those are usually adjacent in this supermarket chain”). If your prediction is wrong, you are surprised (“No, it’s actually the vegetables section. I didn’t expect that!”) and thus rewarded. This makes you more motivated to look around the corner in the future, exploring new locations just to see if your expectations about them meet the reality (and, hopefully, stumble upon the cheese).

Similarly, the ICM method builds a predictive model of the dynamics of the world and gives the agent rewards when the model fails to make good predictions — a marker of surprise or novelty. Note that exploring unvisited locations is not directly a part of the ICM curiosity formulation. For the ICM method, visiting them is only a way to obtain more “surprise” and thus maximize overall rewards. As it turns out, in some environments there could be other ways to inflict self-surprise, leading to unforeseen results.
Agent imbued with surprise-based curiosity gets stuck when it encounters TV. GIF adopted from a video by © Deepak Pathak, used under CC BY 2.0 license.
The Dangers of “Procrastination”
In "Large-Scale Study of Curiosity-Driven Learning", the authors of the ICM method along with researchers from OpenAI show a hidden danger of surprise maximization: agents can learn to indulge procrastination-like behaviour instead of doing something useful for the task at hand. To see why, consider a common thought experiment the authors call the “noisy TV problem”, in which an agent is put into a maze and tasked with finding a highly rewarding item (akin to “cheese” in our previous supermarket example). The environment also contains a TV for which the agent has the remote control. There is a limited number of channels (each with a distinct show) and every press on the remote control switches to a random channel. How would an agent perform in such an environment?

For the surprise-based curiosity formulation, changing channels would result in a large reward, as each change is unpredictable and surprising. Crucially, even after cycling through all the available channels, the random channel selection ensures every new change will still be surprising — the agent is making predictions about what will be on the TV after a channel change, and will very likely be wrong, leading to surprise. Importantly, even if the agent has already seen every show on every channel, the change is still unpredictable. Because of this, the agent imbued with surprise-based curiosity would eventually stay in front of the TV forever instead of searching for a highly rewarding item — akin to procrastination. So, what would be a definition of curiosity which does not lead to such behaviour?

Episodic Curiosity
In “Episodic Curiosity through Reachability”, we explore an episodic memory-based curiosity model that turns out to be less prone to “self-indulging” instant gratification. Why so? Using our example above, after changing channels for a while, all of the shows will end up in memory. Thus, the TV won’t be so attractive anymore: even if the order of shows appearing on the screen is random and unpredictable, all those shows are already in memory! This is the main difference to the surprise-based methods: our method doesn’t even try to make bets about the future which could be hard (or even impossible) to predict. Instead, the agent examines the past to know if it has seen observations similar to the current one. Thus our agent won’t be drawn that much to the instant gratification provided by the noisy TV. It will have to go and explore the world outside of the TV to get more reward.

But how do we decide whether the agent is seeing the same thing as an existing memory? Checking for an exact match could be meaningless: in a realistic environment, the agent rarely sees exactly the same thing twice. For example, even if the agent returned to exactly the same room, it would still see this room under a different angle compared to its memories.

Instead of checking for an exact match in memory, we use a deep neural network that is trained to measure how similar two experiences are. To train this network, we have it guess whether two observations were experienced close together in time, or far apart in time. Temporal proximity is a good proxy for whether two experiences should be judged to be part of the same experience. This training leads to a general concept of novelty via reachability which is illustrated below.
Graph of reachabilities would determine novelty. In practice, this graph is not available — so we train a neural network approximator to estimate a number of steps between observations.
Experimental Results
To compare the performance of different approaches to curiosity, we tested them in two visually rich 3D environments: ViZDoom and DMLab. In those environments, the agent was tasked with various problems like searching for a goal in a maze or collecting good and avoiding bad objects. The DMLab environment happens to provide the agent with a laser-like science fiction gadget. The standard setting in the previous work on DMLab was to equip the agent with this gadget for all tasks, and if the agent does not need a gadget for a particular task, it is free not to use it. Interestingly, similar to the noisy TV experiment described above, the surprise-based ICM method actually uses this gadget a lot even when it is useless for the task at hand! When tasked with searching for a high-rewarding item in the maze, it instead prefers to spend time tagging walls because this yields a lot of “surprise” reward. Theoretically, predicting the result of tagging should be possible, but in practice is too hard as it apparently requires a deeper knowledge of physics than is available to a standard agent.
Surprise-based ICM method is persistently tagging the wall instead of exploring the maze.
Our method instead learns reasonable exploration behaviour under the same conditions. This is because it does not try to predict the result of its actions, but rather seeks observations which are “harder” to achieve from those already in the episodic memory. In other words, the agent implicitly pursues goals which require more effort to reach from memory than just a single tagging action.
Our method shows reasonable exploration.
It is interesting to see that our approach to granting reward penalizes an agent running in circles. This is because after completing the first circle the agent does not encounter new observations other than those in memory, and thus receives no reward:
Our reward visualization: red means negative reward, green means positive reward. Left to right: map with rewards, map with locations currently in memory, first-person view.
At the same time, our method favors good exploration behavior:
Our reward visualization: red means negative reward, green means positive reward. Left to right: map with rewards, map with locations currently in memory, first-person view.
We hope that our work will help lead to a new wave of exploration methods, going beyond surprise and learning more intelligent exploration behaviours. For an in-depth analysis of our method, please take a look at the preprint of our research paper.

Acknowledgements:
This project is a result of a collaboration between the Google Brain team, DeepMind and ETH Zürich. The core team includes Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap and Sylvain Gelly. We would like to thank Olivier Pietquin, Carlos Riquelme, Charles Blundell and Sergey Levine for the discussions about the paper. We are grateful to Indira Pasko for the help with illustrations.

References:
[1] "Count-Based Exploration with Neural Density Models", Georg Ostrovski, Marc G. Bellemare, Aaron van den Oord, Remi Munos
[2] "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning", Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
[3] "Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration", Alexandre Péré, Sébastien Forestier, Olivier Sigaud, Pierre-Yves Oudeyer
[4] "VIME: Variational Information Maximizing Exploration", Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

Source: Google AI Blog


Open-sourcing DeepMind Lab

Originally posted on DeepMind Blog

DeepMind's scientific mission is to push the boundaries of AI, developing systems that can learn to solve any complex problem without needing to be taught how. To achieve this, we work from the premise that AI needs to be general. Agents should operate across a wide range of tasks and be able to automatically adapt to changing circumstances. That is, they should not be pre-programmed, but rather, able to learn automatically from their raw inputs and reward signals from the environment. There are two parts to this research program: (1)  designing ever-more intelligent agents capable of more-and-more sophisticated cognitive skills, and (2) building increasingly complex environments where agents can be trained and evaluated.

The development of innovative agents goes hand in hand with the careful design and implementation of rationally selected, flexible and well-maintained environments. To that end, we at DeepMind have invested considerable effort toward building rich simulated environments to serve as  “laboratories” for AI research. Now we are open-sourcing our flagship platform,  DeepMind Lab, so the broader research community can make use of it.

DeepMind Lab is a fully 3D game-like platform tailored for agent-based AI research. It is observed from a first-person viewpoint, through the eyes of the simulated agent. Scenes are rendered with rich science fiction-style visuals. The available actions allow agents to look around and move in 3D. The agent’s “body” is a floating orb. It levitates and moves by activating thrusters opposite its desired direction of movement, and it has a camera that moves around the main sphere as a ball-in-socket joint tracking the rotational look actions. Example tasks include collecting fruit, navigating in mazes, traversing dangerous passages while avoiding falling off cliffs, bouncing through space using launch pads to move between platforms, playing laser tag, and quickly learning and remembering random procedurally generated environments. An illustration of how agents in DeepMind Lab perceive and interact with the world can be seen below:

At each moment in time, agents observe the world as an image, in pixels, rendered from their own first-person perspective. They also may receive a reward (or punishment!) signal. The agent can activate its thrusters to move in 3D and can also rotate its viewpoint along both horizontal and vertical axes.


Artificial general intelligence research in DeepMind Lab emphasizes navigation, memory, 3D vision from a first person viewpoint, motor control, planning, strategy, time, and fully autonomous agents that must learn for themselves what tasks to perform by exploring their environment. All these factors make learning difficult. Each are considered frontier research questions in their own right. Putting them all together in one platform, as we have, represents a significant new challenge for the field.


DeepMind Lab is highly customisable and extendable. New levels can be authored with off-the-shelf editor tools. In addition, DeepMind Lab includes an interface for programmatic level-creation. Levels can be customised with gameplay logic, item pickups, custom observations, level restarts, reward schemes, in-game messages and more. The interface can be used to create levels in which novel map layouts are generated on the fly while an agent trains. These features are useful in, for example, testing how an agent copes with unfamiliar environments. Users will be able to add custom levels to the platform via GitHub. The assets will be hosted on GitHub alongside all the code, maps and level scripts. Our hope is that the community will help us shape and develop the platform going forward.



DeepMind Lab has been used internally at DeepMind for some time (example). We believe it has already had a significant impact on our thinking concerning numerous aspects of intelligence, both natural and artificial. However, our efforts so far have only barely scratched the surface of what is possible in DeepMind Lab. There are opportunities for significant contributions still to be made in a number of mostly still untouched research domains now available through DeepMind Lab, such as navigation, memory and exploration.

As well as facilitating agent evaluation, there are compelling reasons to think that it may be fundamentally easier to develop intelligence in a 3D world, observed from a first-person viewpoint, like DeepMind Lab. After all, the only known examples of general-purpose intelligence in the natural world arose from a combination of evolution, development, and learning, grounded in physics and the sensory apparatus of animals. It is possible that a large fraction of animal and human intelligence is a direct consequence of the richness of our environment, and unlikely to arise without it. Consider the alternative: if you or I had grown up in a world that looked like Space Invaders or Pac-Man, it doesn’t seem likely we would have achieved much general intelligence!

Read the full paper here.

Access DeepMind's GitHub repository here.

By Charlie Beattie, Joel Leibo, Stig Petersen and Shane Legg, DeepMind Team


How Robots Can Acquire New Skills from Their Shared Experience



The ability to learn from experience will likely be a key in enabling robots to help with complex real-world tasks, from assisting the elderly with chores and daily activities, to helping us in offices and hospitals, to performing jobs that are too dangerous or unpleasant for people. However, if each robot must learn its full repertoire of skills for these tasks only from its own experience, it could take far too long to acquire a rich enough range of behaviors to be useful. Could we bridge this gap by making it possible for robots to collectively learn from each other’s experiences?

While machine learning algorithms have made great strides in natural language understanding and speech recognition, the kind of symbolic high-level reasoning that allows people to communicate complex concepts in words remains out of reach for machines. However, robots can instantaneously transmit their experience to other robots over the network - sometimes known as "cloud robotics" - and it is this ability that can let them learn from each other.

This is true even for seemingly simple low-level skills. Humans and animals excel at adaptive motor control that integrates their senses, reflexes, and muscles in a closely coordinated feedback loop. Robots still struggle with these basic skills in the real world, where the variability and complexity of the environment demands well-honed behaviors that are not easily fooled by distractors. If we enable robots to transmit their experiences to each other, could they learn to perform motion skills in close coordination with sensing in realistic environments?

We previously wrote about how multiple robots could pool their experiences to learn a grasping task. Here, we will discuss new experiments that we conducted to investigate three possible approaches for general-purpose skill learning across multiple robots: learning motion skills directly from experience, learning internal models of physics, and learning skills with human assistance. In all three cases, multiple robots shared their experiences to build a common model of the skill. The skills learned by the robots are still relatively simple -- pushing objects and opening doors -- but by learning such skills more quickly and efficiently through collective learning, robots might in the future acquire richer behavioral repertoires that could eventually make it possible for them to assist us in our daily lives.

Learning from raw experience with model-free reinforcement learning.
Perhaps one of the simplest ways for robots to teach each other is to pool information about their successes and failures in the world. Humans and animals acquire many skills by direct trial-and-error learning. During this kind of ‘model-free’ learning -- so called because there is no explicit model of the environment formed -- they explore variations on their existing behavior and then reinforce and exploit the variations that give bigger rewards. In combination with deep neural networks, model-free algorithms have recently proved to be surprisingly effective and have been key to successes with the Atari video game system and playing Go. Having multiple robots allows us to experiment with sharing experiences to speed up this kind of direct learning in the real world.

In these experiments we tasked robots with trying to move their arms to goal locations, or reaching to and opening a door. Each robot has a copy of a neural network that allows it to estimate the value of taking a given action in a given state. By querying this network, the robot can quickly decide what actions might be worth taking in the world. When a robot acts, we add noise to the actions it selects, so the resulting behavior is sometimes a bit better than previously observed, and sometimes a bit worse. This allows each robot to explore different ways of approaching a task. Records of the actions taken by the robots, their behaviors, and the final outcomes are sent back to a central server. The server collects the experiences from all of the robots and uses them to iteratively improve the neural network that estimates value for different states and actions. The model-free algorithms we employed look across both good and bad experiences and distill these into a new network that is better at understanding how action and success are related. Then, at regular intervals, each robot takes a copy of the updated network from the server and begins to act using the information in its new network. Given that this updated network is a bit better at estimating the true value of actions in the world, the robots will produce better behavior. This cycle can then be repeated to continue improving on the task. In the video below, a robot explores the door opening task.
With a few hours of practice, robots sharing their raw experience learn to make reaches to targets, and to open a door by making contact with the handle and pulling. In the case of door opening, the robots learn to deal with the complex physics of the contacts between the hook and the door handle without building an explicit model of the world, as can be seen in the example below:
Learning how the world works by interacting with objects.
Direct trial-and-error reinforcement learning is a great way to learn individual skills. However, humans and animals don’t learn exclusively by trial and error. We also build mental models about our environment and imagine how the world might change in response to our actions.

We can start with the simplest of physical interactions, and have our robots learn the basics of cause and effect from reflecting on their own experiences. In this experiment, we had the robots play with a wide variety of common household objects by randomly prodding and pushing them inside a tabletop bin. The robots again shared their experiences with each other and together built a single predictive model that attempted to forecast what the world might look like in response to their actions. This predictive model can make simple, if slightly blurry, forecasts about future camera images when provided with the current image and a possible sequence of actions that the robot might execute:
Top row: robotic arms interacting with common household items.
Bottom row: Predicted future camera images given an initial image and a sequence of actions.
Once this model is trained, the robots can use it to perform purposeful manipulations, for example based on user commands. In our prototype, a user can command the robot to move a particular object simply by clicking on that object, and then clicking on the point where the object should go:
The robots in this experiment were not told anything about objects or physics: they only see that the command requires a particular pixel to be moved to a particular place. However, because they have seen so many object interactions in their shared past experiences, they can forecast how particular actions will affect particular pixels. In order for such an implicit understanding of physics to emerge, the robots must be provided with a sufficient breadth of experience. This requires either a lot of time, or sharing the combined experiences of many robots. An extended video on this project may be found here.
Learning with the help of humans.
So far, we discussed how robots can learn entirely on their own. However, human guidance is important, not just for telling the robot what to do, but also for helping the robots along. We have a lot of intuition about how various manipulation skills can be performed, and it only seems natural that transferring this intuition to robots can help them learn these skills a lot faster. In the next experiment, we provided each robot with a different door, and guided each of them by hand to show how these doors can be opened. These demonstrations are encoded into a single combined strategy for all robots, called a policy. The policy is a deep neural network which converts camera images to robot actions, and is maintained on a central server. The following video shows the instructor demonstrating the door-opening skill to a robot:
Next, the robots collectively improve this policy through a trial-and-error learning process. Each robot attempts to open its own door using the latest available policy, with some added noise for exploration. These attempts allow each robot to plan a better strategy for opening the door the next time around, and improve the policy accordingly:
Not surprisingly, we find that robots learn more effectively if they are trained on a curriculum of tasks that are gradually increasing in difficulty. In our experiment, each robot starts off by practicing the door-opening skill on a specific position and orientation of the door that the instructor had previously shown it. As it gets better at performing the task, the instructor starts to alter the position and orientation of the door to be just a bit beyond the current capabilities of the policy, but not so difficult that it fails entirely. This allows the robots to gradually increase their skill level over time, and expands the range of situations they can handle. The combination of human-guidance with trial-and-error learning allowed the robots to collectively learn the skill of door-opening in just a couple of hours. Since the robots were trained on doors that look different from each other, the final policy succeeds on a door with a handle that none of the robots had seen before:
In all three of the experiments described above, the ability to communicate and exchange their experiences allows the robots to learn more quickly and effectively. This becomes particularly important when we combine robotic learning with deep learning, as is the case in all of the experiments discussed above. We’ve seen before that deep learning works best when provided with ample training data. For example, the popular ImageNet benchmark uses over 1.5 million labeled examples. While such a quantity of data is not impossible for a single robot to gather over a few years, it is much more efficient to gather the same volume of experience from multiple robots over the course of a few weeks. Besides faster learning times, this approach might benefit from the greater diversity of experience: a real-world deployment might involve multiple robots in different places and different settings, sharing heterogeneous, varied experiences to build a single highly generalizable representation.

Of course, the kinds of behaviors that robots today can learn are still quite limited. Even basic motion skills, such as picking up objects and opening doors, remain in the realm of cutting edge research. In all of these experiments, a human engineer is still needed to tell the robots what they should learn to do by specifying a detailed objective function. However, as algorithms improve and robots are deployed more widely, their ability to share and pool their experiences could be instrumental for enabling them to assist us in our daily lives.

The experiments on learning by trial-and-error were conducted by Shixiang (Shane) Gu and Ethan Holly from the Google Brain team, and Timothy Lillicrap from DeepMind. Work on learning predictive models was conducted by Chelsea Finn from the Google Brain team, and the research on learning from demonstration was conducted by Yevgen Chebotar, Ali Yahya, Adrian Li, and Mrinal Kalakrishnan from X. We would also like to acknowledge contributions by Peter Pastor, Gabriel Dulac-Arnold, and Jon Scholz. Articles about each of the experiments discussed in this blog post can be found below:

Deep Reinforcement Learning for Robotic Manipulation. Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine. [video]

Deep Visual Foresight for Planning Robot Motion. Chelsea Finn, Sergey Levine. [video] [data]

Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.
Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine.  [video]

Path Integral Guided Policy Search. Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine. [video]

DeepMind moves to TensorFlow



At DeepMind, we conduct state-of-the-art research on a wide range of algorithms, from deep learning and reinforcement learning to systems neuroscience, towards the goal of building Artificial General Intelligence. A key factor in facilitating rapid progress is the software environment used for research. For nearly four years, the open source Torch7 machine learning library has served as our primary research platform, combining excellent flexibility with very fast runtime execution, enabling rapid prototyping. Our team has been proud to contribute to the open source project in capacities ranging from occasional bug fixes to being core maintainers of several crucial components.

With Google’s recent open source release of TensorFlow, we initiated a project to test its suitability for our research environment. Over the last six months, we have re-implemented more than a dozen different projects in TensorFlow to develop a deeper understanding of its potential use cases and the tradeoffs for research. Today we are excited to announce that DeepMind will start using TensorFlow for all our future research. We believe that TensorFlow will enable us to execute our ambitious research goals at much larger scale and an even faster pace, providing us with a unique opportunity to further accelerate our research programme.

As one of the core contributors of Torch7, I have had the pleasure of working closely with an excellent community of developers and researchers, and it has been amazing to see all the great work that has been built on top of the platform and the impact this has had on the field. Torch7 is currently being used by Facebook, Twitter, and many start-ups and academic labs as well as DeepMind, and I’m proud of the significant contribution it has made to a large community in both research and industry. Our transition to TensorFlow represents a new chapter, and I feel very excited about the prospect of DeepMind contributing heavily to another great open source machine learning platform that everyone can use to advance the state-of-the-art.