Dynalang is a novel agent designed to enhance how machines understand and interact with language in relation to visual contexts. Unlike conventional agents that simply follow direct commands, Dynalang leverages diverse language inputs to predict future events and states in the environment. This approach connects language comprehension with the ability to foresee outcomes, creating a self-supervised learning model that learns from past experiences to inform future actions. The agent is capable of processing both language and visual data, allowing it to act effectively in various scenarios, from simple grid worlds to complex, photorealistic environments. The key features of Dynalang include its ability to learn from both real-time interactions and pre-existing datasets without requiring explicit actions or rewards. It uses language hints to describe future observations, correct actions, and outline environmental dynamics, significantly broadening its range of understanding. For example, it can interpret phrases like "The plates are in the kitchen" to make predictive decisions. This multimodal capability positions Dynalang to excel in a variety of tasks, often surpassing traditional reinforcement learning algorithms and specialized models. The research highlights Dynalang's flexibility in learning different forms of language, which enhances its problem-solving skills across multiple tasks. Notably, it can also generate language based on its understanding and conduct pretraining with text-only datasets. This versatility opens up new avenues for integrating language generation and comprehension in a single framework, making Dynalang a significant advancement in the field of agent-based learning.