#Internship Opportunities: Researcher #ReinforcementLearning for #Game Intelligence
Cambridge, Cambridgeshire, United Kingdom, #Microsoft #AI and Research
This is an exceptional opportunity to drive ambitious research while collaborating with a diverse team. Key research challenges we are currently tackling include, but are not limited to, robustness and generalization in (#deep) #RL, multi-agent RL, sample-efficiency and scalability of RL algorithms. The focus and scope of internship projects considers the team’s direction as well as successful candidates’ experience and research interests.
Link
#Job
🔭 @DeepGravity
Cambridge, Cambridgeshire, United Kingdom, #Microsoft #AI and Research
This is an exceptional opportunity to drive ambitious research while collaborating with a diverse team. Key research challenges we are currently tackling include, but are not limited to, robustness and generalization in (#deep) #RL, multi-agent RL, sample-efficiency and scalability of RL algorithms. The focus and scope of internship projects considers the team’s direction as well as successful candidates’ experience and research interests.
Link
#Job
🔭 @DeepGravity
Microsoft
Internship Opportunities: Researcher Reinforcement Learning for Game Intelligence in Cambridge, Cambridgeshire, United Kingdom…
Apply for Internship Opportunities: Researcher Reinforcement Learning for Game Intelligence job with Microsoft in Cambridge, Cambridgeshire, United Kingdom. Research at Microsoft
Multi-Task #ReinforcementLearning without
Interference
While deep reinforcement learning systems have demonstrated impressive results in domains ranging from game playing and robotic control, sample efficiency remains a major challenge, particularly as these algorithms learn individual tasks from scratch. Multi-task and goal-conditioned reinforcement learning have emerged as promising approaches for sharing structure across multiple tasks to enable more efficient learning. However, challenges in optimization have hamstrung such methods from realizing efficiency gains compared to learning tasks independently from scratch. Motivated by these challenges, we develop a general approach that can change the multi-task optimization landscape to alleviate conflicting gradients across tasks. In particular, we introduce two instantiations of this approach, one architectural and one algorithmic, that prevent gradients for different tasks from interfering with one another. On two challenging multi-task RL problems, we find that our approaches leads to greater final performance and learning efficiency in comparison to prior approaches.
Paper
🔭 @DeepGravity
Interference
While deep reinforcement learning systems have demonstrated impressive results in domains ranging from game playing and robotic control, sample efficiency remains a major challenge, particularly as these algorithms learn individual tasks from scratch. Multi-task and goal-conditioned reinforcement learning have emerged as promising approaches for sharing structure across multiple tasks to enable more efficient learning. However, challenges in optimization have hamstrung such methods from realizing efficiency gains compared to learning tasks independently from scratch. Motivated by these challenges, we develop a general approach that can change the multi-task optimization landscape to alleviate conflicting gradients across tasks. In particular, we introduce two instantiations of this approach, one architectural and one algorithmic, that prevent gradients for different tasks from interfering with one another. On two challenging multi-task RL problems, we find that our approaches leads to greater final performance and learning efficiency in comparison to prior approaches.
Paper
🔭 @DeepGravity
Meta-gradient updates for training return functions for #ReinforcementLearning systems,
Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum return function G so that the training of the system is improved. This provides a more effective and efficient means of training a reinforcement learning system as the system is able to converge on an optimum set of one or more policy parameters θ more quickly by training the return function G as it goes. In particular, the return function G is made dependent on the one or more policy parameters θ and a meta-objective function J′ is used that is differentiated with respect to the one or more return parameters η to improve the training of the return function G.
#Google
#DeepMind
Paper
🔭 @DeepGravity
Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum return function G so that the training of the system is improved. This provides a more effective and efficient means of training a reinforcement learning system as the system is able to converge on an optimum set of one or more policy parameters θ more quickly by training the return function G as it goes. In particular, the return function G is made dependent on the one or more policy parameters θ and a meta-objective function J′ is used that is differentiated with respect to the one or more return parameters η to improve the training of the return function G.
#DeepMind
Paper
🔭 @DeepGravity
Google
Meta-gradient updates for training return functions for reinforcement learning systems
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum…
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta #ReinforcementLearning
Abstract: #Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on meta-reinforcement learning focuses on task distributions that are very narrow. For example, a commonly used meta-reinforcement learning benchmark uses different running velocities for a simulated robot as different tasks. When policies are meta-trained on such narrow task distributions, they cannot possibly generalize to more quickly acquire entirely new tasks. Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors. In this paper, we propose an open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks. Our aim is to make it possible to develop algorithms that generalize to accelerate the acquisition of entirely new, held-out tasks. We evaluate 6 state-of-the-art metareinforcement learning and multi-task learning algorithms on these tasks. Surprisingly, while each task and its variations (e.g., with different object positions) can be learned with reasonable success, these algorithms struggle to learn with multiple tasks at the same time, even with as few as ten distinct training tasks. Our analysis and open-source environments pave the way for future research in multi-task learning and meta-learning that can enable meaningful generalization, thereby unlocking the full potential of these methods.
Paper
🔭 @DeepGravity
Abstract: #Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on meta-reinforcement learning focuses on task distributions that are very narrow. For example, a commonly used meta-reinforcement learning benchmark uses different running velocities for a simulated robot as different tasks. When policies are meta-trained on such narrow task distributions, they cannot possibly generalize to more quickly acquire entirely new tasks. Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors. In this paper, we propose an open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks. Our aim is to make it possible to develop algorithms that generalize to accelerate the acquisition of entirely new, held-out tasks. We evaluate 6 state-of-the-art metareinforcement learning and multi-task learning algorithms on these tasks. Surprisingly, while each task and its variations (e.g., with different object positions) can be learned with reasonable success, these algorithms struggle to learn with multiple tasks at the same time, even with as few as ten distinct training tasks. Our analysis and open-source environments pave the way for future research in multi-task learning and meta-learning that can enable meaningful generalization, thereby unlocking the full potential of these methods.
Paper
🔭 @DeepGravity
Learning human objectives by evaluating hypothetical behaviours
TL;DR: We present a method for training #ReinforcementLearning agents from human feedback in the presence of unknown unsafe states.
#DeepMind
Link
🔭 @DeepGravity
TL;DR: We present a method for training #ReinforcementLearning agents from human feedback in the presence of unknown unsafe states.
#DeepMind
Link
🔭 @DeepGravity
Deepmind
Learning human objectives by evaluating hypothetical behaviours
We present a new method for training reinforcement learning agents from human feedback in the presence of unknown unsafe states.
#ReinforcementLearning for ArtiSynth
This repository holds the plugin for the #biomechanical simulation environment of ArtiSynth. The purpose of this work is to bridge in between the biomechanical and reinforcement learning domains of research.
Link
🔭 @DeepGravity
This repository holds the plugin for the #biomechanical simulation environment of ArtiSynth. The purpose of this work is to bridge in between the biomechanical and reinforcement learning domains of research.
Link
🔭 @DeepGravity
GitHub
GitHub - amir-abdi/artisynth-rl: Reinforcement Learning plugin and models for ArtiSynth
Reinforcement Learning plugin and models for ArtiSynth - GitHub - amir-abdi/artisynth-rl: Reinforcement Learning plugin and models for ArtiSynth
Our reinforcement learning architect designs have been just published on #NeurIPS2019 AI Art Gallery:
https://lnkd.in/dFZ37BN
Seems it draws like a baby now, but is growing and hopefully would be a skillful #RL artist very soon.
#reinforcementlearning #deeplearning #ai #artificialintelligence #art #deepreinforcementlearning #creativeart #neurips
🔭 @DeepGravity
https://lnkd.in/dFZ37BN
Seems it draws like a baby now, but is growing and hopefully would be a skillful #RL artist very soon.
#reinforcementlearning #deeplearning #ai #artificialintelligence #art #deepreinforcementlearning #creativeart #neurips
🔭 @DeepGravity
AI Art Gallery
Yuta Akizuki, Mathias Bernhard, Reza Kakooee, Marirena Kladeftira, Benjamin Dillenburger - AI Art Gallery
Generative Modelling with Design Constraints – Reinforcement Learning for Furniture Generation (2019) Generative design has been…
#ReinforcementLearning and #Control as #ProbabilisticInference: Tutorial and Review
The framework of reinforcement learning or #OptimalControl provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables effective reasoning about uncertainty, the connection between reinforcement learning and inference in probabilistic models is not immediately obvious. However, such a connection has considerable value when it comes to algorithm design: formalizing a problem as probabilistic inference in principle allows us to bring to bear a wide array of approximate inference tools, extend the model in flexible and powerful ways, and reason about compositionality and partial observability. In this article, we will discuss how a generalization of the reinforcement learning or optimal control problem, which is sometimes termed maximum entropy reinforcement learning, is equivalent to exact probabilistic inference in the case of deterministic dynamics, and variational inference in the case of stochastic dynamics. We will present a detailed derivation of this framework, overview prior work that has drawn on this and related ideas to propose new reinforcement learning and control algorithms, and describe perspectives on future research.
Link
🔭 @DeepGravity
The framework of reinforcement learning or #OptimalControl provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables effective reasoning about uncertainty, the connection between reinforcement learning and inference in probabilistic models is not immediately obvious. However, such a connection has considerable value when it comes to algorithm design: formalizing a problem as probabilistic inference in principle allows us to bring to bear a wide array of approximate inference tools, extend the model in flexible and powerful ways, and reason about compositionality and partial observability. In this article, we will discuss how a generalization of the reinforcement learning or optimal control problem, which is sometimes termed maximum entropy reinforcement learning, is equivalent to exact probabilistic inference in the case of deterministic dynamics, and variational inference in the case of stochastic dynamics. We will present a detailed derivation of this framework, overview prior work that has drawn on this and related ideas to propose new reinforcement learning and control algorithms, and describe perspectives on future research.
Link
🔭 @DeepGravity
Interestingness Elements for Explainable #ReinforcementLearning: Understanding Agents' Capabilities and Limitations
We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual explanations of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans in correctly perceiving the aptitude of agents with different characteristics, including their capabilities and limitations, given explanations automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents' aptitude in the task, and determine when they might need adjustments to improve their performance.
Link
🔭 @DeepGravity
We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual explanations of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans in correctly perceiving the aptitude of agents with different characteristics, including their capabilities and limitations, given explanations automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents' aptitude in the task, and determine when they might need adjustments to improve their performance.
Link
🔭 @DeepGravity
Explaining #ReinforcementLearning: Active vs Passive
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
Article
🔭 @DeepGravity
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
Article
🔭 @DeepGravity
KDnuggets
Explaining Reinforcement Learning: Active vs Passive - KDnuggets
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.