Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. Utilizing recent advances in performance bounds for inverse reinforcement learning, we propose a risk-aware active inverse reinforcement learning algorithm that focuses active queries on areas of the state space with the potential for large generalization error. We show that risk-aware active learning outperforms standard active IRL approaches on gridworld, simulated driving, and table setting tasks, while also providing a performance-based stopping criterion that allows a robot to know when it has received enough demonstrations to safely perform a task.

4
下载
关闭预览

相关内容

Performance:International Symposium on Computer Performance Modeling, Measurements and Evaluation。 Explanation:计算机性能建模、测量和评估国际研讨会。 Publisher:ACM。 SIT:http://dblp.uni-trier.de/db/conf/performance/

Deep reinforcement learning suggests the promise of fully automated learning of robotic control policies that directly map sensory inputs to low-level actions. However, applying deep reinforcement learning methods on real-world robots is exceptionally difficult, due both to the sample complexity and, just as importantly, the sensitivity of such methods to hyperparameters. While hyperparameter tuning can be performed in parallel in simulated domains, it is usually impractical to tune hyperparameters directly on real-world robotic platforms, especially legged platforms like quadrupedal robots that can be damaged through extensive trial-and-error learning. In this paper, we develop a stable variant of the soft actor-critic deep reinforcement learning algorithm that requires minimal hyperparameter tuning, while also requiring only a modest number of trials to learn multilayer neural network policies. This algorithm is based on the framework of maximum entropy reinforcement learning, and automatically trades off exploration against exploitation by dynamically and automatically tuning a temperature parameter that determines the stochasticity of the policy. We show that this method achieves state-of-the-art performance on four standard benchmark environments. We then demonstrate that it can be used to learn quadrupedal locomotion gaits on a real-world Minitaur robot, learning to walk from scratch directly in the real world in two hours of training.

0
5
下载
预览

In this paper, we propose an inverse reinforcement learning method for architecture search (IRLAS), which trains an agent to learn to search network structures that are topologically inspired by human-designed network. Most existing architecture search approaches totally neglect the topological characteristics of architectures, which results in complicated architecture with a high inference latency. Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt). To avoid raising a too strong prior over the search space, we introduce inverse reinforcement learning to train the mirror stimuli function and exploit it as a heuristic guidance for architecture search, easily generalized to different architecture search algorithms. On CIFAR-10, the best architecture searched by our proposed IRLAS achieves 2.60% error rate. For ImageNet mobile setting, our model achieves a state-of-the-art top-1 accuracy 75.28%, while being 2~4x faster than most auto-generated architectures. A fast version of this model achieves 10% faster than MobileNetV2, while maintaining a higher accuracy.

0
4
下载
预览

Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the robot's hypothesis space. When this is not true, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. We focus specifically on learning from physical human corrections during the robot's task execution, where not having a rich enough hypothesis space leads to the robot updating its objective in ways that the person did not actually intend. We observe that such corrections appear irrelevant to the robot, because they are not the best way of achieving any of the candidate objectives. Instead of naively trusting and learning from every human interaction, we propose robots learn conservatively by reasoning in real time about how relevant the human's correction is for the robot's hypothesis space. We test our inference method in an experiment with human interaction data, and demonstrate that this alleviates unintended learning in an in-person user study with a 7DoF robot manipulator.

0
3
下载
预览

The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent's updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.

0
3
下载
预览

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.

0
9
下载
预览

We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.

0
5
下载
预览

Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of agent interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated by those between a single agent and the average effect from the overall population or neighboring agents; the interplay between the two entities is mutually reinforced: the learning of the individual agent's optimal policy depends on the dynamics of the population, while the dynamics of the population change according to the collective patterns of the individual policies. We develop practical mean field Q-learning and mean field Actor-Critic algorithms and analyze the convergence of the solution to Nash equilibrium. Experiments on Gaussian squeeze, Ising model, and battle games justify the learning effectiveness of our mean field approaches. In addition, we report the first result to solve the Ising model via model-free reinforcement learning methods.

0
3
下载
预览

Policy gradient methods are widely used in reinforcement learning algorithms to search for better policies in the parameterized policy space. They do gradient search in the policy space and are known to converge very slowly. Nesterov developed an accelerated gradient search algorithm for convex optimization problems. This has been recently extended for non-convex and also stochastic optimization. We use Nesterov's acceleration for policy gradient search in the well-known actor-critic algorithm and show the convergence using ODE method. We tested this algorithm on a scheduling problem. Here an incoming job is scheduled into one of the four queues based on the queue lengths. We see from experimental results that algorithm using Nesterov's acceleration has significantly better performance compared to algorithm which do not use acceleration. To the best of our knowledge this is the first time Nesterov's acceleration has been used with actor-critic algorithm.

0
6
下载
预览

Caching and rate allocation are two promising approaches to support video streaming over wireless network. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled QoE-driven video rate allocation problem. We establish a mathematical model for this problem, and point out that it is difficult to solve the problem with traditional dynamic programming. Then we propose a deep reinforcement learning approaches to solve it. First, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience of mobile user moving among small cells. We also investigate the impact of configuration of critical parameters on the performance of our algorithm.

0
4
下载
预览

Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. Here, we draw inspiration from this to highlight a simple technique by which deep recurrent networks can similarly exploit their prior knowledge to learn a useful representation for a new word from little data. This could make natural language processing systems much more flexible, by allowing them to learn continually from the new words they encounter.

0
5
下载
预览
小贴士
相关论文
Tuomas Haarnoja,Aurick Zhou,Sehoon Ha,Jie Tan,George Tucker,Sergey Levine
5+阅读 · 2018年12月26日
IRLAS: Inverse Reinforcement Learning for Architecture Search
Minghao Guo,Zhao Zhong,Wei Wu,Dahua Lin,Junjie Yan
4+阅读 · 2018年12月14日
Andreea Bobu,Andrea Bajcsy,Jaime F. Fisac,Anca D. Dragan
3+阅读 · 2018年10月11日
Multi-task Deep Reinforcement Learning with PopArt
Matteo Hessel,Hubert Soyer,Lasse Espeholt,Wojciech Czarnecki,Simon Schmitt,Hado van Hasselt
3+阅读 · 2018年9月12日
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Iulian Vlad Serban,Chinnadhurai Sankar,Michael Pieper,Joelle Pineau,Yoshua Bengio
9+阅读 · 2018年7月12日
Relational Deep Reinforcement Learning
Vinicius Zambaldi,David Raposo,Adam Santoro,Victor Bapst,Yujia Li,Igor Babuschkin,Karl Tuyls,David Reichert,Timothy Lillicrap,Edward Lockhart,Murray Shanahan,Victoria Langston,Razvan Pascanu,Matthew Botvinick,Oriol Vinyals,Peter Battaglia
5+阅读 · 2018年6月28日
Yaodong Yang,Rui Luo,Minne Li,Ming Zhou,Weinan Zhang,Jun Wang
3+阅读 · 2018年6月12日
K. Lakshmanan
6+阅读 · 2018年4月24日
Zhengming Zhang,Yaru Zheng,Meng Hua,Yongming Huang,Luxi Yang
4+阅读 · 2018年3月30日
Andrew K. Lampinen,James L. McClelland
5+阅读 · 2017年10月27日
相关VIP内容
专知会员服务
11+阅读 · 2020年4月28日
专知会员服务
85+阅读 · 2020年2月1日
【强化学习资源集合】Awesome Reinforcement Learning
专知会员服务
44+阅读 · 2019年12月23日
Stabilizing Transformers for Reinforcement Learning
专知会员服务
23+阅读 · 2019年10月17日
Keras François Chollet 《Deep Learning with Python 》, 386页pdf
专知会员服务
55+阅读 · 2019年10月12日
强化学习最新教程,17页pdf
专知会员服务
58+阅读 · 2019年10月11日
相关资讯
Hierarchically Structured Meta-learning
CreateAMind
10+阅读 · 2019年5月22日
Transferring Knowledge across Learning Processes
CreateAMind
6+阅读 · 2019年5月18日
逆强化学习-学习人先验的动机
CreateAMind
5+阅读 · 2019年1月18日
强化学习的Unsupervised Meta-Learning
CreateAMind
7+阅读 · 2019年1月7日
无监督元学习表示学习
CreateAMind
20+阅读 · 2019年1月4日
Unsupervised Learning via Meta-Learning
CreateAMind
27+阅读 · 2019年1月3日
meta learning 17年:MAML SNAIL
CreateAMind
9+阅读 · 2019年1月2日
Hierarchical Imitation - Reinforcement Learning
CreateAMind
15+阅读 · 2018年5月25日
Reinforcement Learning: An Introduction 2018第二版 500页
CreateAMind
9+阅读 · 2018年4月27日
强化学习 cartpole_a3c
CreateAMind
9+阅读 · 2017年7月21日
Top