Reinforcement learning (RL) agents have achieved remarkable success in various domains, such as game-playing and protein structure prediction. However, most RL agents rely on exploration to find optimal solutions without explicit guidance. This paper proposes a methodology for training RL agents using text-based instructions in Dravidian Languages, including Telugu, Tamil, and Malayalam along with using the English language. The agents are trained in a modified Lunar Lander environment, where they must follow specific paths to successfully land the lander. The methodology involves collecting a dataset of human demonstrations and textual instructions, encoding the instructions into numerical representations using text-based embeddings, and training RL agents using state-of-the-art algorithms. The results demonstrate that the trained Soft Actor-Critic (SAC) agent can effectively understand and generalize instructions in different languages, outperforming other RL algorithms such as Proximal Policy Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG).

This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.