  Robots and animals both experience the world through their bodies and senses.
Their embodiment constrains their experiences, ensuring they unfold
continuously in space and time. As a result, the experiences of embodied agents
are intrinsically correlated. Correlations create fundamental challenges for
machine learning, as most techniques rely on the assumption that data are
independent and identically distributed. In reinforcement learning, where data
are directly collected from an agent's sequential experiences, violations of
this assumption are often unavoidable. Here, we derive a method that overcomes
this issue by exploiting the statistical mechanics of ergodic processes, which
we term maximum diffusion reinforcement learning. By decorrelating agent
experiences, our approach provably enables single-shot learning in continuous
deployments over the course of individual task attempts. Moreover, we prove our
approach generalizes well-known maximum entropy techniques, and robustly
exceeds state-of-the-art performance across popular benchmarks. Our results at
the nexus of physics, learning, and control form a foundation for transparent
and reliable decision-making in embodied reinforcement learning agents.
