Dynamic adaptation and learning, akin to natural organisms, is crucial for robots operating in real-world scenarios like search and rescue missions. We propose a solution combining intuition from embodied evolution and Bayes theory to promote flexible exploration in foraging tasks. Our investigation focuses on three main areas: 1) leveraging communication and prior knowledge to develop adaptable strategies in agent groups, 2) addressing challenges from sparse rewards or limited data availability, and 3) developing methods for concurrent evaluation and training in a single iteration, filling a current gap in learning-based solutions. Future directions include exploring decentralized coordination among agents and incorporating assistance based on prospective memory and altruism in multi-agent reinforcement learning.