The five idioms (i.e., varieties) of the Romansh language are largely standardized and are taught in the schools of the respective communities in Switzerland. In this paper, we present the first parallel corpus of Romansh idioms. The corpus is based on 291 schoolbook volumes, which are comparable in content for the five idioms. We use automatic alignment methods to extract 207k multi-parallel segments from the books, with more than 2M tokens in total. A small-scale human evaluation confirms that the segments are highly parallel, making the dataset suitable for NLP applications such as machine translation between Romansh idioms. We release the parallel and unaligned versions of the dataset under a CC-BY-NC-SA license and demonstrate its utility for machine translation by training and evaluating an LLM on a sample of the dataset.

Actor-critic methods, a type of model-free reinforcement learning (RL), have achieved state-of-the-art performances in many real-world domains in continuous control. Despite their success, the wide-scale deployment of these models is still a far cry. The main problems in these actor-critic methods are inefficient exploration and sub-optimal policies. Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic Policy Gradient (TD3), two cutting edge such algorithms, suffer from these issues. SAC effectively addressed the problems of sample complexity and convergence brittleness to hyper-parameters and thus outperformed all state-of-the-art algorithms including TD3 in harder tasks, whereas TD3 produced moderate results in all environments. SAC suffers from inefficient exploration owing to the Gaussian nature of its policy which causes borderline performance in simpler tasks. In this paper, we introduce Opportunistic Actor-Critic (OPAC), a novel model-free deep RL algorithm that employs better exploration policy and lesser variance. OPAC combines some of the most powerful features of TD3 and SAC and aims to optimize a stochastic policy in an off-policy way. For calculating the target Q-values, instead of two critics, OPAC uses three critics and based on the environment complexity, opportunistically chooses how the target Q-value is computed from the critics' evaluation. We have systematically evaluated the algorithm on MuJoCo environments where it achieves state-of-the-art performance and outperforms or at least equals the performance of TD3 and SAC.