Continuously learning to solve unseen tasks with limited experience has been extensively pursued in meta-learning and continual learning, but with restricted assumptions such as accessible task distributions, independently and identically distributed tasks, and clear task delineations. However, real-world physical tasks frequently violate these assumptions, resulting in performance degradation. This paper proposes a continual online model-based reinforcement learning approach that does not require pre-training to solve task-agnostic problems with unknown task boundaries. We maintain a mixture of experts to handle nonstationarity, and represent each different type of dynamics with a Gaussian Process to efficiently leverage collected data and expressively model uncertainty. We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference. Our approach reliably handles the task distribution shift by generating new models for never-before-seen dynamics and reusing old models for previously seen dynamics. In experiments, our approach outperforms alternative methods in non-stationary tasks, including classic control with changing dynamics and decision making in different driving scenarios.
From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e.g., web pages). We propose a novel optimization objective for this setting that has several practically desirable properties, and efficient algorithms for it with optimality guarantees even in the face of mixed content change observability and initially unknown change model parameters. Experiments on 18.5M URLs crawled daily for 14 weeks show significant advantages of this approach over prior art.
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Proactive process adaptation can prevent and mitigate upcoming problems during process execution by using predictions about how an ongoing case will unfold. There is an important trade-off with respect to these predictions: Earlier predictions leave more time for adaptations than later predictions, but earlier predictions typically exhibit a lower accuracy than later predictions, because not much information about the ongoing case is available. An emerging solution to address this trade-off is to continuously generate predictions and only trigger proactive adaptations when prediction reliability is greater than a predefined threshold. However, a good threshold is not known a priori. One solution is to empirically determine the threshold using a subset of the training data. While an empirical threshold may be optimal for the training data used and the given cost structure, such a threshold may not be optimal over time due to non-stationarity of process environments, data, and cost structures. Here, we use online reinforcement learning as an alternative solution to learn when to trigger proactive process adaptations based on the predictions and their reliability at run time. Experimental results for three public data sets indicate that our approach may on average lead to 12.2% lower process execution costs compared to empirical thresholding.
The first step towards moving RL towards a data driven paradigm is to considerthe general idea of offline (batch) RL. Offline RL considers the problem oflearning optimal policies from arbitrary off-policy data, without any furtherexploration. This is able to eliminate the data collection problem in RL, andincorporate data from arbitrary sources including other robots orteleoperation. However, depending on the quality of available data and theproblem being tackled, we will often need to augment offline training withtargeted online improvement. This problem setting actually has uniquechallenges of its own. In this blog post, we discuss how we can move RL fromtraining from scratch with every new problem to a paradigm which is able toreuse prior data effectively, with some offline training followed by onlinefinetuning.
We aim to study tasks representative of the difficulties of real-world robotlearning, where offline learning and online fine-tuning are most relevant. Onesuch setting is the suite of dexterous manipulation tasks proposed by Rajeswaran et al., 2017. Thesetasks involve complex manipulation skills using a 28-DoF five-fingered hand inthe MuJoCo simulator: in-hand rotation of a pen, opening a door by unlatchingthe handle, and picking up a sphere and relocating it to a target location.These environments exhibit many challenges: high dimensional action spaces,complex manipulation physics with many intermittent contacts, and randomizedhand and object positions. The reward functions in these environments arebinary 0-1 rewards for task completion. Rajeswaran et al. provide 25 humandemonstrations for each task, which are not fully optimal but do solve thetask. Since this dataset is very small, we generated another 500 trajectoriesof interaction data by constructing a behavioral cloned policy, and thensampling from this policy.
Being able to use prior data and fine-tune quickly on new problems opens upmany new avenues of research. We are most excited about using AWAC to move fromthe single-task regime in RL to the multi-task regime, with data sharing andgeneralization between tasks. The strength of deep learning has been itsability to generalize in open-world settings, which we have already seentransform the fields of computer vision and natural language processing. Toachieve the same type of generalization in robotics, we will need RL algorithmsthat take advantage of vast amounts of prior data. But one key distinction inrobotics is that collecting high-quality data for a task is very difficult -often as difficult as solving the task itself. This is opposed to, for instancecomputer vision, where humans can label the data. Thus, the active datacollection (online learning) will be an important piece of the puzzle.
This work also suggests a number of algorithmic directions to move forward.Note that in this work we focused on mismatched action distributions betweenthe policy $\pi$ and the behavior data $\pi_\beta$. When doing off-policylearning, there is also a mismatched marginal state distribution between thetwo. Intuitively, consider a problem with two solutions A and B, with B being ahigher return solution and off-policy data demonstrating solution A provided.Even if the robot discovers solution B during online exploration, theoff-policy data still consists of mostly data from path A. Thus the Q-functionand policy updates are computed over states encountered while traversing path Aeven though it will not encounter these states when executing the optimalpolicy. This problem has been studied previously. Accounting for bothtypes of distribution mismatch will likely result in better RL algorithms.
Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In many real-world problems, the environments are commonly dynamic, in which the performance of reinforcement learning approaches can degrade drastically. A direct cause of the performance degradation is the high-variance and biased estimation of the reward, due to the distribution shifting in dynamic environments. In this paper, we propose two techniques to alleviate the unstable reward estimation problem in dynamic environments, the stratified sampling replay strategy and the approximate regretted reward, which address the problem from the sample aspect and the reward aspect, respectively. Integrating the two techniques with Double DQN, we propose the Robust DQN method. We apply Robust DQN in the tip recommendation system in Taobao online retail trading platform. We firstly disclose the highly dynamic property of the recommendation application. We then carried out online A/B test to examine Robust DQN. The results show that Robust DQN can effectively stabilize the value estimation and, therefore, improves the performance in this real-world dynamic environment.
Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment between LLMs' knowledge and the environment can be wrong and limit functional competence due to lack of grounding. In this paper, we study an approach (named GLAM) to achieve this alignment through functional grounding: we consider an agent using an LLM as a policy that is progressively updated as the agent interacts with the environment, leveraging online Reinforcement Learning to improve its performance to solve goals. Using an interactive textual environment designed to study higher-level forms of functional grounding, and a set of spatial and navigation tasks, we study several scientific questions: 1) Can LLMs boost sample efficiency for online learning of various RL tasks? 2) How can it boost different forms of generalization? 3) What is the impact of online learning? We study these questions by functionally grounding several variants (size, architecture) of FLAN-T5.
Overextended educators face the daunting task of making online school attendance enjoyable for students, without the natural positive reinforcement, such as socializing with friends, that in-person schooling provides. However, most teachers have many ways to engage learners in the classroom that can be adapted to engaging those in online settings. One such tool is the use of frequent and specific positive reinforcement.
2) Activity Reinforcers: Educators also may find activity reinforcers easy to implement in an online setting. These include special privileges, classroom jobs, or time to devote to a fun activity. Activity reinforcers can be used to motivate individual students, such as by assigning a highly engaged student to monitor the chat for questions or to press the record button in a synchronous session. Activity reinforcers also can be used with groups, such as letting the whole class wear a funny hat or providing unstructured time for students to talk to peers in breakout rooms. be457b7860
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