Social learning strategies are a key component of human intelligence and of our ability of learning from and collaborating with other humans in their environment. Inspired by this, some initial research efforts are enabling embodied AI agents with social and cooperative skills, thus permitting them to coordinate and collaborate with and to learn from other agents and humans. The goal of this PhD thesis is to devise innovative cooperative learning algorithms to support navigation of embodied AI agents in dynamic complex environments populated by other agents and humans, and to learn how to interact and to communicate with other heterogenous agents to learn collaboratively world models. The ideal candidate would have research interest in cooperative and embodied AI, multi-agent deep reinforcement learning, neuro-symbolic approaches, etc. The candidate will have the possibility of working within the ELLIS network and in collaboration with top international and national universities.