Safe Multi-Agent Reinforcement Learning (MARL) typically relies on manually specified numeric cost functions to ensure that policy behaviours respect safety constraints. As systems scale and human-defined constraints become more diverse, context-dependent, and frequently updated, hand-crafting such cost functions becomes prohibitively complex, tedious, and error-prone. Natural language offers an intuitive and flexible alternative for defining constraints, enabling broader accessibility and easier adaptation to new scenarios and evolving rules. However, current MARL frameworks lack effective mechanisms to incorporate free-form textual constraints in a robust and principled way. To bridge this gap, we introduce Safe Multi-Agent Reinforcement Learning with natural Language constraints (SMALL), a framework that leverages fine-tuned language models to parse and encode textual constraints into semantically meaningful embeddings. These embeddings characterise prohibited states or behaviours and enable automatic prediction of constraint violations. We integrate the resulting learned costs directly into MARL training, allowing agents to optimise task performance while simultaneously minimising constraint violations, without requiring manually engineered numeric cost functions. To rigorously evaluate our method, we also propose the LaMaSafe benchmark---a set of diverse multi-agent tasks designed to assess the capability of MARL algorithms to understand and adhere to realistic, human-provided natural language constraints. Experimental results across LaMaSafe environments show that SMALL achieves comparable task performance to strong MARL baselines while significantly reducing constraint violations. While SMALL does not provide formal safety guarantees, it demonstrates that natural language can be used to shape multi-agent behaviour toward safer policies.

Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies. Humans primarily rely on embodied visual perception to safely navigate and interact with their surroundings, making it a valuable capability for RL agents. However, existing vision-based 3D benchmarks only consider simple navigation tasks. To address this shortcoming, we introduce **HASARD**, a suite of diverse and complex tasks to **HA**rness **SA**fe **R**L with **D**oom, requiring strategic decision-making, comprehending spatial relationships, and predicting the short-term future. HASARD features three difficulty levels and two action spaces. An empirical evaluation of popular baseline methods demonstrates the benchmark's complexity, unique challenges, and reward-cost trade-offs. Visualizing agent navigation during training with top-down heatmaps provides insight into a method's learning process. Incrementally training across difficulty levels offers an implicit learning curriculum. HASARD is the first safe RL benchmark to exclusively target egocentric vision-based learning, offering a cost-effective and insightful way to explore the potential and boundaries of current and future safe RL methods. The environments and baseline implementations are open-sourced.

The advancement of continual reinforcement learning (RL) has been facing various obstacles, including standardized metrics and evaluation protocols, demanding computational requirements, and a lack of widely accepted standard benchmarks. In response to these challenges, we present COOM ($\textbf{C}$ontinual D$\textbf{OOM}$), a continual RL benchmark tailored for embodied pixel-based RL. COOM presents a meticulously crafted suite of task sequences set within visually distinct 3D environments, serving as a robust evaluation framework to assess crucial aspects of continual RL, such as catastrophic forgetting, knowledge transfer, and sample-efficient learning. Following an in-depth empirical evaluation of popular continual learning (CL) methods, we pinpoint their limitations, provide valuable insight into the benchmark and highlight unique algorithmic challenges. This makes our work the first to benchmark image-based CRL in 3D environments with embodied perception. The primary objective of the COOM benchmark is to offer the research community a valuable and cost-effective challenge. It seeks to deepen our comprehension of the capabilities and limitations of current and forthcoming CL methods in an RL setting. The code and environments are open-sourced and accessible on GitHub.

Despite the recent success of deep reinforcement learning (RL), the generalization ability of RL agents remains an open problem for real-world applicability. RL agents trained on pixels may completely be derailed from achieving their objectives in unseen situations with different levels of visual changes. However, numerous existing RL suites do not address this as a primary objective or lack consistent level design of increased complexity. In this paper, we introduce the LevDoom benchmark, a suite containing semi-realistic 3D simulation environments with coherent levels of difficulty in the renowned video game Doom, designed to benchmark generalization in vision-based RL. We demonstrate how our benchmark reveals weaknesses of some popular Deep RL algorithms, which fail to prevail in modified environments. We further establish how our difficulty level design presents increasing complexity to these algorithms.