AI Technology & Industry Review
A DeepMind research team explores the problem of how to train agents to collaborate well with novel human partners without using human data and presents Fictitious Co-Play (FCP), a surprisingly simple approach designed to address this challenge.
In the paper Introducing Symmetries to Black Box Meta Reinforcement Learning, a research team from DeepMind and The Swiss AI Lab IDSIA explores the role of symmetries in meta generalization and shows that introducing more symmetries to black-box meta-learners can improve their ability to generalize to unseen action and observation spaces, tasks, and environments.
A research team from Carnegie Mellon University, Google Brain and UC Berkeley proposes a robust predictable control (RPC) method for learning reinforcement learning policies that use fewer bits of information. This simple and theoretically-justified algorithm achieves much tighter compression, is more robust, and generalizes better than prior methods, achieving up to 5× higher rewards than a standard information bottleneck.
Researchers from Robotics at Google propose reformulating behavioural cloning using implicit models and show that this simple change can lead to remarkable improvements in performance across a wide range of contact-rich robot policy learning.
In the paper ReGen: Reinforcement Learning for Text and Knowledge Base Generation Using Pretrained Language Models, IBM researchers present ReGen, a bidirectional generation of text and graph that leverages reinforcement learning to push the performance of text-to-graph and graph-to-text generation tasks to a higher level.
A research team from Stanford University introduces BEHAVIOR, a benchmark for embodied AI with 100 realistic, diverse and complex everyday household activities in simulation. BEHAVIOR addresses challenges such as definition, instantiation in a simulator, and evaluation; and pushes the state-of-the-art by adding new types of state changes.
A DeepMind research team proposes Collect and Infer, a novel paradigm that explicitly models Reinforcement Learning (RL) as data collection and knowledge inference to dramatically boost RL data efficiency.
A research team from the University of California, Princeton University and ETH Zurich proposes RLQP, an accelerated QP solver based on operator-splitting QP (OSQP) that uses deep reinforcement learning (RL) to speed up the solver’s convergence rate.
A research team from DeepMind and Amii extends the emphatic method to multi-step deep reinforcement learning (RL) targets, and demonstrates that combining emphatic trace with deep neural networks can improve performance on classic Atari video games.
A research team from Mila, McGill University, Université de Montréal, DeepMind and Microsoft proposes GFlowNet, a novel flow network-based generative method that can turn a given positive reward into a generative policy that samples with a probability proportional to the return.v
A research team from McGill University, Université de Montréal, DeepMind and Mila presents an end-to-end, model-based deep reinforcement learning (RL) agent that dynamically attends to relevant parts of its environments to facilitate out-of-distribution (OOD) and systematic generalization.
A research team from UC Berkeley, Facebook AI Research and Google Brain abstracts Reinforcement Learning (RL) as a sequence modelling problem. Their proposed Decision Transformer simply outputs optimal actions by leveraging a causally masked transformer, yet matches or exceeds state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.
A research team from Google Brain conducts a comprehensive empirical study on more than fifty choices in a generic adversarial imitation learning framework and explores their impacts on large-scale (>500k trained agents) continuous-control tasks to provide practical insights and recommendations for designing novel and effective AIL algorithms.
A research team from the University of Montreal and Max Planck Institute for Intelligent Systems constructs a reinforcement learning agent whose knowledge and reward function can be reused across tasks, along with an attention mechanism that dynamically selects unchangeable knowledge pieces to enable out-of-distribution adaptation and generalization.
A research team from MIT and MIT-IBM Watson AI Lab proposes Curious Representation Learning (CRL), a framework that learns to understand the surrounding environment by training a reinforcement learning (RL) agent to maximize the error of a representation learner to gain an incentive to explore the environment.
A research team from UC Berkeley and Carnegie Mellon University proposes a task-agnostic reinforcement learning method that reduces the task-specific engineering required for domain randomization of both visual and dynamics parameters.
A research team from DeepMind introduces Anakin and Sebulba, two architectures that demonstrate reinforcement learning platforms based on TPUs can efficiently deliver exceptional performance at scale and with low cost.
Stanford researchers’ DERL (Deep Evolutionary Reinforcement Learning) is a novel computational framework that enables AI agents to evolve morphologies and learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information.
A research team from DeepMind and University College London have released Alchemy, a novel open-source benchmark for meta-RL research.
Researchers from Vector Institute, University of Toronto and Google Brain recently studied three common types of intrinsic motivation across seven agents, three Atari games, and the 3D game Minecraft.
This year, 22 Transformer-related research papers were accepted by NeurIPS, the world’s most prestigious machine learning conference. Synced has selected ten of these works to showcase the latest Transformer trends.
A new DeepMind scalable environment simulator takes a digital approach to the question, enabling the examination of environmental factors on AI agents.
A new study proposes using human feedback and interaction logs to boost offline reinforcement learning (RL) in natural language processing (NLP).
DeepMind and Universidade de Lisboa introduce graph-based toolkit for analyzing and comparing multiplayer games
In a new paper, researchers from Google, OpenAI, and DeepMind introduce “behaviour priors,” a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts.
Japanese AI startup Preferred Networks (PFN) is moving ChainerRL to the PyTorch ecosystem.
Augmented Temporal Contrast (ATC), a new unsupervised learning (UL) task for learning visual representations agnostic to rewards and without degrading the control policy.
The AI Economist uses a two-level RL framework where a collection of AI agents simulate how humans might react to various taxes in a principled economic simulation.
Researchers from Stanford University, UC Berkeley and CMU review neuromechanical simulations and DRL, with a focus on modelling the control of human locomotion.
DeepMind and McGill University researchers believe the problem could be solved through a strategic “divide-and-conquer” approach.
Researchers conducted a thorough empirical study of hyperparameter selection for offline RL, aiming to identify and develop more reliable and effective approaches.
PFRL succeeds ChainerRL as comprehensive library with cutting-edge deep reinforcement learning algorithms and features.
Researchers proposed a Taylor Expansion Policy Optimization (TayPO) framework that combines two leading algorithmic improvement methods.
DeepMind researchers call attention to another “fundamentally novel” development in AI research — deep reinforcement learning (deep RL) — which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists.
NLE is a procedurally generated environment for testing the robustness and systematic generalization of RL agents.
DeepMind researchers released several new models and a tutorial for their dm_control software stack for physics-based simulation and reinforcement learning environments using MuJoCo physics.
Researchers from the University of Tokyo and Google Research have proposed a new metric for RL performance and novel BREMEN algorithm designed to manage the costs and risks of new policy deployment.
DeepMind researchers introduce a framework that aims to solve the problem by enabling simple RL agent implementations to be run at different scales of execution.
Enter Plan2Explore — a self-supervised RL agent designed to quickly generalize to unseen tasks in a zero or few-shot manner.
The proposed concurrent DRL algorithm enables robots to take a broader and more long-term view of tasks and behaviours, and decide on their next action before the current one is completed.