Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning

In value-based reinforcement learning, removing the target network is tempting as the boostrapped target would be built from up-to-date estimates, and the spared memory occupied by the target network could be reallocated to expand the capacity of the online network. However, eliminating the target network introduces instability, leading to a decline in performance. Removing the target network also means we cannot leverage the literature developed around target networks. In this work, we propose to use a copy of the last linear layer of the online network as a target network, while sharing the remaining parameters with the up-to-date online network, hence stepping out of the binary choice between target-based and target-free methods. It enables us to leverage the concept of iterated Q-learning, which consists of learning consecutive Bellman iterations in parallel, to reduce the performance gap between target-free and target-based approaches. Our findings demonstrate that this novel method, termed iterated Shared Q-Learning (iS-QL), improves the sample efficiency of target-free approaches across various settings. Importantly, iS-QL requires a smaller memory footprint and comparable training time to classical target-based algorithms, highlighting its potential to scale reinforcement learning research.

Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning

Recent works have successfully demonstrated that sparse deep reinforcement learning agents can be competitive against their dense counterparts. This opens up opportunities for reinforcement learning applications in fields where inference time and memory requirements are cost-sensitive or limited by hardware. Until now, dense-to-sparse methods have relied on hand-designed sparsity schedules that are not synchronized with the agent's learning pace. Crucially, the final sparsity level is chosen as a hyperparameter, which requires careful tuning as setting it too high might lead to poor performances. In this work, we address these shortcomings by crafting a dense-to-sparse algorithm that we name Eau De $Q$-Network (EauDeQN). To increase sparsity at the agent's learning pace, we consider multiple online networks with different sparsity levels, where each online network is trained from a shared target network. At each target update, the online network with the smallest loss is chosen as the next target network, while the other networks are replaced by a pruned version of the chosen network. We evaluate the proposed approach on the Atari $2600$ benchmark and the MuJoCo physics simulator, showing that EauDeQN reaches high sparsity levels while keeping performances high.

InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?

Recent advancements in language technology and Artificial Intelligence have resulted in numerous Language Models being proposed to perform various tasks in the legal domain ranging from predicting judgments to generating summaries. Despite their immense potential, these models have been proven to learn and exhibit societal biases and make unfair predictions. In this study, we explore the ability of Large Language Models (LLMs) to perform legal tasks in the Indian landscape when social factors are involved. We present a novel metric, $\beta$-weighted $\textit{Legal Safety Score ($LSS_{\beta}$)}$, which encapsulates both the fairness and accuracy aspects of the LLM. We assess LLMs' safety by considering its performance in the $\textit{Binary Statutory Reasoning}$ task and its fairness exhibition with respect to various axes of disparities in the Indian society. Task performance and fairness scores of LLaMA and LLaMA--2 models indicate that the proposed $LSS_{\beta}$ metric can effectively determine the readiness of a model for safe usage in the legal sector. We also propose finetuning pipelines, utilising specialised legal datasets, as a potential method to mitigate bias and improve model safety. The finetuning procedures on LLaMA and LLaMA--2 models increase the $LSS_{\beta}$, improving their usability in the Indian legal domain. Our code is publicly released.