Weaknesses: - The method section looks not self-contained and lacks descriptions of some key components. In particular: * What is Eq.(9) for? Why "the SL is the negative logarithm of a polynomial in \theta" -- where is the "negative logarithm" in Eq.(9)? It looks its practical implementation is discussed in the "Evaluating the Semantic Loss" part (L.140) which involves the Weighted Model Count (WMC) and knowledge compilation (KC). However, no details about KC are presented.

This work aims at estimating generative distributions of structured objects that satisfy certain semantic constraints (in first-order logic). The authors achieve this goal by adding a "semantic loss" to the GAN's learning objective and using Knowledge compilation (KC) to build a circuit that allows efficient evaluation. Experiments on game-level generation tasks and a molecule generation task support the proposed method. Strengths: i) Incorporating structured constraints in GAN models is both intellectually and practically interesting; ii) The experiments are comprehensive and convincing in most cases; and iii) the paper is clearly written for most parts. The paper is recommended for acceptance.

Most current AI models have little ability to store and later retrieve a record or representation of what they do. In human cognition, episodic memories play an important role in both recall of the past as well as planning for the future. The ability to form and use episodic memories would similarly enable a broad range of improved capabilities in an AI agent that interacts with and takes actions in the world. Researchers have begun directing more attention to developing memory abilities in AI models. It is therefore likely that models with such capability will be become widespread in the near future. This could in some ways contribute to making such AI agents safer by enabling users to better monitor, understand, and control their actions. However, as a new capability with wide applications, we argue that it will also introduce significant new risks that researchers should begin to study and address. We outline these risks and benefits and propose four principles to guide the development of episodic memory capabilities so that these will enhance, rather than undermine, the effort to keep AI safe and trustworthy.

There has been a plethora of recent and historical work on this topic, finding different ways to help networks alleviate the issue of catastrophic forgetting --- where a network trained on tasks A_0 through A_i, forgets these to differing degrees when trained on tasks A_i 1 onward. Most methods can be divided into regularisation based, memory based or meta-learning based. One relatively recent work is GEM (gradient of episodic memory) (and relatedly A-GEM). This works by storing examples from seen tasks in an episodic memory. When learning a new task, the gradient update is modified such that it does not increase the loss on examples from previous tasks (these are represented by the examples in memory).

The paper introduces a clear, simple generalisation of two established continual learning methods (GEM and A-GEM) which performs very well in a thorough empirical evaluation. All reviewers and the AC value the effort that the authors put in their response. There is consensus that the work has merit and all reviewers recommend accepting the paper (R1 and R4 raised their score).

The authors do a good job of motivating their work, and they contribute a nice experimental section with good results. The ablation study was thorough. Well done! --- Many tasks that might be given to an RL agent are impossible without working memory. This paper presents a suite of tasks which require use of that memory in order to succeed. These tasks are compiled from a variety of other sources, either directly or re-implemented for this suite.

The reviewers consider the task suite, the memory-augmented model, and the evaluations to be solid contributions. Please be sure to release the task suite...

Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory. We propose a new model-based episodic memory of trajectories addressing current limitations of episodic control. Built upon the memory, we construct a complementary learning model via a dynamic hybrid control unifying model-based, episodic and habitual learning into a single architecture. Experiments demonstrate that our model allows significantly faster and better learning than other strong reinforcement learning agents across a variety of environments including stochastic and non-Markovian settings.

Social learning, a cornerstone of cultural evolution, enables individuals to acquire knowledge by observing and imitating others. At the heart of its efficacy lies episodic memory, which encodes specific behavioral sequences to facilitate learning and decision-making. This study explores the interrelation between episodic memory and social learning in collective foraging. Using Sequential Episodic Control (SEC) agents capable of sharing complete behavioral sequences stored in episodic memory, we investigate how variations in the frequency and fidelity of social learning influence collaborative foraging performance. Furthermore, we analyze the effects of social learning on the content and distribution of episodic memories across the group. High-fidelity social learning is shown to consistently enhance resource collection efficiency and distribution, with benefits sustained across memory lengths. In contrast, low-fidelity learning fails to outperform nonsocial learning, spreading diverse but ineffective mnemonic patterns. Novel analyses using mnemonic metrics reveal that high-fidelity social learning also fosters mnemonic group alignment and equitable resource distribution, while low-fidelity conditions increase mnemonic diversity without translating to performance gains. Additionally, we identify an optimal range for episodic memory length in this task, beyond which performance plateaus. These findings underscore the critical effects of social learning on mnemonic group alignment and distribution and highlight the potential of neurocomputational models to probe the cognitive mechanisms driving cultural evolution.

Humans navigate unfamiliar environments using episodic simulation and episodic memory, which facilitate a deeper understanding of the complex relationships between environments and objects. Developing an imaginative memory system inspired by human mechanisms can enhance the navigation performance of embodied agents in unseen environments. However, existing Vision-and-Language Navigation (VLN) agents lack a memory mechanism of this kind. To address this, we propose a novel architecture that equips agents with a reality-imagination hybrid memory system. This system enables agents to maintain and expand their memory through both imaginative mechanisms and navigation actions. Additionally, we design tailored pre-training tasks to develop the agent's imaginative capabilities. Our agent can imagine high-fidelity RGB images for future scenes, achieving state-of-the-art result in Success rate weighted by Path Length (SPL).