Category Archives: Artificial Intelligence

Survey and benchmarking of open-source, low-cost LLMs for generating program code

December 12, 2024 16:41 ,

Jessica López Espejel, Mahaman Sanoussi Yahaya Alassan, Merieme Bouhandi, Walid Dahhane, El Hassane Ettifouri, Low-cost language models: Survey and performance evaluation on Python code generation, Engineering Applications of Artificial Intelligence, Volume 140, 2025, DOI: 10.1016/j.engappai.2024.109490.

Large Language Models (LLMs) have become a popular choice for many Natural Language Processing (NLP) tasks due to their versatility and ability to produce high-quality results. Specifically, they are increasingly used for automatic code generation to help developers tackle repetitive coding tasks. However, LLMs’ substantial computational and memory requirements often make them inaccessible to users with limited resources. This paper focuses on very low-cost models which offer a more accessible alternative to resource-intensive LLMs. We notably: (1) propose a thorough semi-manual evaluation of their performance in generating Python code, (2) introduce a Chain-of-Thought (CoT) prompting strategy to improve model reasoning and code quality, and (3) propose a new dataset of 60 programming problems, with varied difficulty levels, designed to extend existing benchmarks like HumanEval and EvalPlus. Our findings show that some low-cost compatible models achieve competitive results compared to larger models like ChatGPT despite using significantly fewer resources. We will make our dataset and prompts publicly available to support further research.

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RL training with a massive amount of scenarios, GPU accelerated

November 21, 2024 12:45 ,

Michael Matthews, Michael Beukman, Chris Lu, Jakob Foerster, Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks, arXiv:2410.23208 [cs.LG].

While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for agents that act in sequential decision problems remains an open challenge. In this work, we take a step towards this goal by procedurally generating tens of millions of 2D physics-based tasks and using these to train a general reinforcement learning (RL) agent for physical control. To this end, we introduce Kinetix: an open-ended space of physics-based RL environments that can represent tasks ranging from robotic locomotion and grasping to video games and classic RL environments, all within a unified framework. Kinetix makes use of our novel hardware-accelerated physics engine Jax2D that allows us to cheaply simulate billions of environment steps during training. Our trained agent exhibits strong physical reasoning capabilities, being able to zero-shot solve unseen human-designed environments. Furthermore, fine-tuning this general agent on tasks of interest shows significantly stronger performance than training an RL agent *tabula rasa*. This includes solving some environments that standard RL training completely fails at. We believe this demonstrates the feasibility of large scale, mixed-quality pre-training for online RL and we hope that Kinetix will serve as a useful framework to investigate this further.

Posted in: Reinforcement learning in AI

Generating intrinsic rewards to address the sparse reward problem of RL

November 14, 2024 16:49 ,

Z. Gao et al., Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning, IEEE Transactions on Artificial Intelligence, vol. 5, no. 11, pp. 5530-5539, Nov. 2024, DOI: 10.1109/TAI.2024.3413692.

In sparse extrinsic reward settings, reinforcement learning remains a challenge despite increasing interest in this field. Existing approaches suggest that intrinsic rewards can alleviate issues caused by reward sparsity. However, many studies overlook the critical role of temporal information, essential for human curiosity. This article introduces a novel intrinsic reward mechanism inspired by human learning processes, where curiosity is evaluated by comparing current observations with historical knowledge. Our method involves training a self-supervised prediction model, periodically saving snapshots of the model parameters, and employing the nuclear norm to assess the temporal inconsistency between predictions from different snapshots as intrinsic rewards. Additionally, we propose a variational weighting mechanism to adaptively assign weights to the snapshots, enhancing the model’s robustness and performance. Experimental results across various benchmark environments demonstrate the efficacy of our approach, which outperforms other state-of-the-art methods without incurring additional training costs and exhibits higher noise tolerance. Our findings indicate that leveraging temporal information in intrinsic rewards can significantly improve exploration performance, motivating future research to develop more robust and accurate reward systems for reinforcement learning.

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Improving sample efficiency under sparse rewards and large continuous action spaces through predictive control in RL

October 10, 2024 09:26 ,

Antonyshyn, L., Givigi, S., Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards, J Intell Robot Syst 110, 100 (2024) DOI: 10.1007/s10846-024-02118-y.

Sparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when considering applications of reinforcement learning to robotics and other cyber-physical systems. This is so because in these domains many tasks are goal-based and naturally expressed with binary successes and failures, action spaces are large and continuous, and real interactions with the environment are limited. In this work, we propose Deep Value-and-Predictive-Model Control (DVPMC), a model-based predictive reinforcement learning algorithm for continuous control that uses system identification, value function approximation and sampling-based optimization to select actions. The algorithm is evaluated on a dense reward and a sparse reward task. We show that it can match the performance of a predictive control approach to the dense reward problem, and outperforms model-free and model-based learning algorithms on the sparse reward task on the metrics of sample efficiency and performance. We verify the performance of an agent trained in simulation using DVPMC on a real robot playing the reach-avoid game. Video of the experiment can be found here: https://youtu.be/0Q274kcfn4c.

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An interesting survey -before the “generative AI” boom- of the integration of sub-symbolic (for learning) and symbolic (for reasoning) systems

September 19, 2024 09:31 ,

Artur d’Avila Garcez, Luis C. Lamb, Neurosymbolic AI: The 3rd Wave, arXiv:2012.05876 [cs.AI] https://arxiv.org/abs/2012.05876v2.

Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability via symbolic representations for network models. In this paper, we relate recent and early research results in neurosymbolic AI with the objective of identifying the key ingredients of the next wave of AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI. We also identify promising directions and challenges for the next decade of AI research from the perspective of neural-symbolic systems.

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A relatively simple way of reducing the sampling cost of DQN

September 19, 2024 08:12 ,

Hossein Hassani, Soodeh Nikan, Abdallah Shami, Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay, Engineering Applications of Artificial Intelligence, Volume 137, Part A, 2024, DOI: 10.1016/j.engappai.2024.109147.

Deep Reinforcement Learning (DRL) models play a fundamental role in autonomous driving applications; however, they typically suffer from sample inefficiency because they often require many interactions with the environment to learn effective policies. This makes the training process time-consuming. To address this shortcoming, Prioritized Experience Replay (PER) has proven to be effective by prioritizing samples with high Temporal-Difference (TD) error for learning. In this context, this study contributes to artificial intelligence by proposing a sample-efficient DRL algorithm called Episodic-Guided Prioritized Experience Replay (EPER). The core innovation of EPER lies in the utilization of an episodic memory, dedicated to storing successful training episodes. Within this memory, expected returns for each state–action pair are extracted. These returns, combined with TD error-based prioritization, form a novel objective function for deep Q-network training. To prevent excessive determinism, EPER introduces exploration into the learning process by incorporating a regularization term into the objective function that allows exploration of state-space regions with diverse Q-values. The proposed EPER algorithm is suitable to train a DRL agent for handling episodic tasks, and it can be integrated into off-policy DRL models. EPER is employed for traffic navigation through scenarios such as highway driving, merging, roundabout, and intersection to showcase its application in engineering. The attained results denote that, compared with the PER and an additional state-of-the-art training technique, EPER is superior in expediting the training of the agent and learning a more optimal policy that leads to lower collision rates within the constructed navigation scenarios.

Posted in: Reinforcement learning in AI , Tagged: ,

Equivalence between Transformers and SVMs

September 12, 2024 11:16 ,

Davoud Ataee Tarzanagh, Yingcong Li, Christos Thrampoulidis, Samet Oymak, Transformers as Support Vector Machines, arXiv:2308.16898 [cs.LG], https://arxiv.org/abs/2308.16898.

Since its inception in “Attention Is All You Need”, transformer architecture has led to revolutionary advancements in NLP. The attention layer within the transformer admits a sequence of input tokens X and makes them interact through pairwise similarities computed as softmax(XQK⊤X⊤), where (K,Q) are the trainable key-query parameters. In this work, we establish a formal equivalence between the optimization geometry of self-attention and a hard-margin SVM problem that separates optimal input tokens from non-optimal tokens using linear constraints on the outer-products of token pairs. This formalism allows us to characterize the implicit bias of 1-layer transformers optimized with gradient descent: (1) Optimizing the attention layer with vanishing regularization, parameterized by (K,Q), converges in direction to an SVM solution minimizing the nuclear norm of the combined parameter W=KQ⊤. Instead, directly parameterizing by W minimizes a Frobenius norm objective. We characterize this convergence, highlighting that it can occur toward locally-optimal directions rather than global ones. (2) Complementing this, we prove the local/global directional convergence of gradient descent under suitable geometric conditions. Importantly, we show that over-parameterization catalyzes global convergence by ensuring the feasibility of the SVM problem and by guaranteeing a benign optimization landscape devoid of stationary points. (3) While our theory applies primarily to linear prediction heads, we propose a more general SVM equivalence that predicts the implicit bias with nonlinear heads. Our findings are applicable to arbitrary datasets and their validity is verified via experiments. We also introduce several open problems and research directions. We believe these findings inspire the interpretation of transformers as a hierarchy of SVMs that separates and selects optimal tokens.

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A novel way of addressing the maximization bias in RL

September 12, 2024 08:34 ,

Martin Waltz, Ostap Okhrin, Addressing maximization bias in reinforcement learning with two-sample testing, Artificial Intelligence, Volume 336, 2024, DOI: 10.1016/j.artint.2024.104204.

Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algorithms and can sometimes lead to dramatic performance decreases or even complete algorithmic failure. We frame the bias problem statistically and consider it an instance of estimating the maximum expected value (MEV) of a set of random variables. We propose the T-Estimator (TE) based on two-sample testing for the mean, that flexibly interpolates between over- and underestimation by adjusting the significance level of the underlying hypothesis tests. We also introduce a generalization, termed K-Estimator (KE), that obeys the same bias and variance bounds as the TE and relies on a nearly arbitrary kernel function. We introduce modifications of Q-Learning and the Bootstrapped Deep Q-Network (BDQN) using the TE and the KE, and prove convergence in the tabular setting. Furthermore, we propose an adaptive variant of the TE-based BDQN that dynamically adjusts the significance level to minimize the absolute estimation bias. All proposed estimators and algorithms are thoroughly tested and validated on diverse tasks and environments, illustrating the bias control and performance potential of the TE and KE.

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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: