Reinforcement learning (RL) agents have demonstrated strong performance in structured environments, yet they continue to struggle in real-world settings where goals are ambiguous, conditions change dynamically, and external supervision is limited. These challenges stem not primarily from the algorithmic limitations but from the characteristics of conventional training environments, which are usually static, task-specific, and externally defined. In contrast, biological agents develop autonomy and adaptivity by interacting with complex, dynamic environments, where most behaviors are ultimately driven by internal physiological needs. Inspired by these biological constraints, we introduce EVAAA (Essential Variables in Autonomous and Adaptive Agents), a 3D virtual environment for training and evaluating egocentric RL agents endowed with internal physiological state variables. In EVAAA, agents must maintain essential variables (EVs)—e.g., satiation, hydration, body temperature, and tissue integrity (the level of damage)—within viable bounds by interacting with environments that increase in difficulty at each stage. The reward system is derived from internal state dynamics, enabling agents to generate goals autonomously without manually engineered, task-specific reward functions. Built on Unity ML-Agents, EVAAA supports multimodal sensory inputs, including vision, olfaction, thermoception, collision, as well as egocentric embodiment. It features naturalistic survival environments for curricular training and a suite of unseen experimental testbeds, allowing for the evaluation of autonomous and adaptive behaviors that emerge from the interplay between internal state dynamics and environmental constraints. By integrating physiological regulation, embodiment, continual learning, and generalization, EVAAA offers a biologically inspired benchmark for studying autonomy, adaptivity, and internally driven control in RL agents. Our code is publicly available at https://github.com/cocoanlab/evaaa