Category Archives: Artificial Intelligence

RL in periodic scenarios

July 18, 2024 12:42 ,

A. Aniket and A. Chattopadhyay, Online Reinforcement Learning in Periodic MDP, IEEE Transactions on Artificial Intelligence, vol. 5, no. 7, pp. 3624-3637, July 2024 DOI: 10.1109/TAI.2024.3375258.

We study learning in periodic Markov decision process (MDP), a special type of nonstationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index and propose a periodic upper confidence bound reinforcement learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies linearly with the period N and as O(TlogT−−−−−√) with the horizon length T . Utilizing the information about the sparsity of transition matrix of augmented MDP, we propose another algorithm [periodic upper confidence reinforcement learning with Bernstein bounds (PUCRLB) which enhances upon PUCRL2, both in terms of regret ( O(N−−√) dependency on period] and empirical performance. Finally, we propose two other algorithms U-PUCRL2 and U-PUCRLB for extended uncertainty in the environment in which the period is unknown but a set of candidate periods are known. Numerical results demonstrate the efficacy of all the algorithms.

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Review of the current methologies for achieving continuous learning, and its biological bases

June 28, 2024 08:32 ,

Buddhi Wickramasinghe, Gobinda Saha , and Kaushik Roy, Continual Learning: A Review of Techniques, Challenges, and Future Directions, IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE, VOL. 5, NO. 6, JUNE 2024 DOI: 10.1109/TAI.2023.3339091.

Continual learning (CL), or the ability to acquire, process, and learn from new information without forgetting acquired knowledge, is a fundamental quality of an intelligent agent. The human brain has evolved into gracefully dealing with ever-changing circumstances and learning from experience with the help of complex neurophysiological mechanisms. Even though artificial intelligence takes after human intelligence, traditional neural networks do not possess the ability to adapt to dynamic environments. When presented with new information, an artificial neural network (ANN) often completely forgets its prior knowledge, a phenomenon called catastrophic forgetting or catastrophic interference. Incorporating CL capabilities into ANNs is an active field of research and is integral to achieving artificial general intelligence. In this review, we revisit CL approaches and critically examine their strengths and limitations. We conclude that CL approaches should look beyond mitigating catastrophic forgetting and strive for systems that can learn, store, recall, and transfer knowledge, much like the human brain. To this end, we highlight the importance of adopting alternative brain-inspired data representations and learning algorithms and provide our perspective on promising new directions where CL could play an instrumental role.

See also: doi: 10.1109/TAI.2024.3355879

Posted in: Artificial Intelligence, Psycho-physiological bases of engineering , Tagged:

Reducing discovered skills in DRL to the essential ones, modelling skills with SMDP Q-learning

June 7, 2024 10:38 ,

Shuai Qing, Fei Zhu, Refine to the essence: Less-redundant skill learning via diversity clustering, Engineering Applications of Artificial Intelligence, Volume 133, Part A, 2024 DOI: 10.1016/j.engappai.2024.107981.

In reinforcement learning, skill is a potentially conditional policy that solves tasks in a hierarchically controlled manner. Progress on skill discovery helps agents learn a set of diverse and useful skills without external supervision to tackle complex tasks with sparse rewards. Although most of the studies have aimed to maximize the diversity of skills discovered, the distinguishability between skills diminishes as the number of skills increases, leading to a subset of similar and redundant skills. To tackle this problem, a method called Refine to the Essence of Skills (RE-Skill) is proposed, which aims at learning skills with less redundancy. RE-Skill integrates the concepts of cluster analysis and policy distillation, clustering similar skills together based on their unique features, learning the most optimal performance within each cluster, and filtering out similar skills that involve excessive and intricate actions, thereby reducing redundancy among skills. By refining clusters of similar skills into less-redundant independent skills, RE-Skill demonstrates superior performance compared to other skill discovery algorithms and shows how these less-redundant skills effectively address downstream tasks, indicating that RE-Skill is able to extend its efficacy to engineering applications in robot control and obstacle training tasks within complex environments.

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A novel RL setting for non-Markovian systems

April 26, 2024 09:38 ,

Ronen I. Brafman, Giuseppe De Giacomo, Regular decision processes, Artificial Intelligence, Volume 331, 2024 DOI: 10.1016/j.artint.2024.104113.

We introduce and study Regular Decision Processes (RDPs), a new, compact model for domains with non-Markovian dynamics and rewards, in which the dependence on the past is regular, in the language theoretic sense. RDPs are an intermediate model between MDPs and POMDPs. They generalize k-order MDPs and can be viewed as a POMDP in which the hidden state is a regular function of the entire history. In factored RDPs, transition and reward functions are specified using formulas in linear temporal logics over finite traces, or using regular expressions. This allows specifying complex dependence on the past using intuitive and compact formulas, and building models of partially observable domains without specifying an underlying state space.

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The problem of incorporating novelties into the knowledge of an AI agent

April 26, 2024 09:34 ,

Shivam Goel, Panagiotis Lymperopoulos, Ravenna Thielstrom, Evan Krause, Patrick Feeney, Pierrick Lorang, Sarah Schneider, Yichen Wei, Eric Kildebeck, Stephen Goss, Michael C. Hughes, Liping Liu, Jivko Sinapov, Matthias Scheutz, A neurosymbolic cognitive architecture framework for handling novelties in open worlds, Artificial Intelligence, Volume 331, 2024 DOI: 10.1016/j.artint.2024.104111.

“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This runs counter to the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging. The types of environments AI agents can be deployed in are limited by the inability to handle the novelties that occur in open world environments. This paper presents a novel cognitive architecture framework to handle open-world novelties. This framework combines symbolic planning, counterfactual reasoning, reinforcement learning, and deep computer vision to detect and accommodate novelties. We introduce general algorithms for exploring open worlds using inference and machine learning methodologies to facilitate novelty accommodation. The ability to detect and accommodate novelties allows agents built on this framework to successfully complete tasks despite a variety of novel changes to the world. Both the framework components and the entire system are evaluated in Minecraft-like simulated environments. Our results indicate that agents are able to efficiently complete tasks while accommodating “concealed novelties” not shared with the architecture development team.

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POMDPs focused on obtaining policies that can be understood well just through the observation of the robot actions

April 3, 2024 12:39 ,

Miguel Faria, Francisco S. Melo, Ana Paiva, “Guess what I’m doing”: Extending legibility to sequential decision tasks, Artificial Intelligence, Volume 330, 2024 DOI: 10.1016/j.artint.2024.104107.

In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoLMDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several scenarios of varying complexity. We also showcase the use of our legible policies as demonstrations in machine teaching scenarios, establishing their superiority in teaching new behaviours against the commonly used demonstrations based on the optimal policy. Finally, we assess the legibility of our computed policies through a user study, where people are asked to infer the goal of a mobile robot following a legible policy by observing its actions.

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On the influence of the representations obtained through Deep RL in the learning process

April 3, 2024 12:35 ,

Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White, Investigating the properties of neural network representations in reinforcement learning, Artificial Intelligence, Volume 330, 2024 DOI: 10.1016/j.artint.2024.104100.

In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning focused on designing fixed-basis architectures to achieve properties thought to be desirable, such as orthogonality and sparsity. In contrast, the idea behind deep reinforcement learning methods is that the agent designer should not encode representational properties, but rather that the data stream should determine the properties of the representation—good representations emerge under appropriate training schemes. In this paper we bring these two perspectives together, empirically investigating the properties of representations that support transfer in reinforcement learning. We introduce and measure six representational properties over more than 25,000 agent-task settings. We consider Deep Q-learning agents with different auxiliary losses in a pixel-based navigation environment, with source and transfer tasks corresponding to different goal locations. We develop a method to better understand why some representations work better for transfer, through a systematic approach varying task similarity and measuring and correlating representation properties with transfer performance. We demonstrate the generality of the methodology by investigating representations learned by a Rainbow agent that successfully transfers across Atari 2600 game modes.

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Object oriented paradigm to improve transfer learning in RL, i.e., a sort of symbolic abstraction mechanism

March 1, 2024 10:05 ,

Ofir Marom, Benjamin Rosman, Transferable dynamics models for efficient object-oriented reinforcement learning, Robotics and Autonomous Systems, Volume 174, 2024 DOI: 10.1016/j.artint.2024.104079.

The Reinforcement Learning (RL) framework offers a general paradigm for constructing autonomous agents that can make effective decisions when solving tasks. An important area of study within the field of RL is transfer learning, where an agent utilizes knowledge gained from solving previous tasks to solve a new task more efficiently. While the notion of transfer learning is conceptually appealing, in practice, not all RL representations are amenable to transfer learning. Moreover, much of the research on transfer learning in RL is purely empirical. Previous research has shown that object-oriented representations are suitable for the purposes of transfer learning with theoretical efficiency guarantees. Such representations leverage the notion of object classes to learn lifted rules that apply to grounded object instantiations. In this paper, we extend previous research on object-oriented representations and introduce two formalisms: the first is based on deictic predicates, and is used to learn a transferable transition dynamics model; the second is based on propositions, and is used to learn a transferable reward dynamics model. In addition, we extend previously introduced efficient learning algorithms for object-oriented representations to our proposed formalisms. Our frameworks are then combined into a single efficient algorithm that learns transferable transition and reward dynamics models across a domain of related tasks. We illustrate our proposed algorithm empirically on an extended version of the Taxi domain, as well as the more difficult Sokoban domain, showing the benefits of our approach with regards to efficient learning and transfer.

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Improving sample efficiency of RL through memory reconstruction

February 15, 2024 09:29 ,

Y. Kang et al., Sample Efficient Reinforcement Learning Using Graph-Based Memory Reconstruction, IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 751-762, Feb. 2024 DOI: 10.1109/TAI.2023.3268612.

Reinforcement learning (RL) algorithms typically require orders of magnitude more interactions than humans to learn effective policies. Research on memory in neuroscience suggests that humans’ learning efficiency benefits from associating their experiences and reconstructing potential events. Inspired by this finding, we introduce a human brainlike memory structure for agents and build a general learning framework based on this structure to improve the RL sampling efficiency. Since this framework is similar to the memory reconstruction process in psychology, we name the newly proposed RL framework as graph-based memory reconstruction (GBMR). In particular, GBMR first maintains an attribute graph on the agent’s memory and then retrieves its critical nodes to build and update potential paths among these nodes. This novel pipeline drives the RL agent to learn faster with its memory-enhanced value functions and reduces interactions with the environment by reconstructing its valuable paths. Extensive experimental analyses and evaluations in the grid maze and some challenging Atari environments demonstrate GBMRs superiority over traditional RL methods. We will release the source code and trained models to facilitate further studies in this research direction.

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On the relations between symbolic and subsymbolic systems in AI

February 9, 2024 09:25 ,

Giuseppe Marra, Sebastijan Duman\u010di\u0107, Robin Manhaeve, Luc De Raedt, From statistical relational to neurosymbolic artificial intelligence: A survey, Artificial Intelligence, Volume 328, 2024 DOI: 10.1016/j.artint.2023.104062.

This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence. Neurosymbolic artificial intelligence (NeSy) studies the integration of symbolic reasoning and neural networks, while statistical relational artificial intelligence (StarAI) focuses on integrating logic with probabilistic graphical models. This survey identifies seven shared dimensions between these two subfields of AI. These dimensions can be used to characterize different NeSy and StarAI systems. They are concerned with (1) the approach to logical inference, whether model or proof-based; (2) the syntax of the used logical theories; (3) the logical semantics of the systems and their extensions to facilitate learning; (4) the scope of learning, encompassing either parameter or structure learning; (5) the presence of symbolic and subsymbolic representations; (6) the degree to which systems capture the original logic, probabilistic, and neural paradigms; and (7) the classes of learning tasks the systems are applied to. By positioning various NeSy and StarAI systems along these dimensions and pointing out similarities and differences between them, this survey contributes fundamental concepts for understanding the integration of learning and reasoning.

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