  In Reinforcement Learning (RL), artificial agents are trained to maximize
numerical rewards by performing tasks. Exploration is essential in RL because
agents must discover information before exploiting it. Two rewards encouraging
efficient exploration are the entropy of action policy and curiosity for
information gain. Entropy is well-established in literature, promoting
randomized action selection. Curiosity is defined in a broad variety of ways in
literature, promoting discovery of novel experiences. One example, prediction
error curiosity, rewards agents for discovering observations they cannot
accurately predict. However, such agents may be distracted by unpredictable
observational noises known as curiosity traps. Based on the Free Energy
Principle (FEP), this paper proposes hidden state curiosity, which rewards
agents by the KL divergence between the predictive prior and posterior
probabilities of latent variables. We trained six types of agents to navigate
mazes: baseline agents without rewards for entropy or curiosity, and agents
rewarded for entropy and/or either prediction error curiosity or hidden state
curiosity. We find entropy and curiosity result in efficient exploration,
especially both employed together. Notably, agents with hidden state curiosity
demonstrate resilience against curiosity traps, which hinder agents with
prediction error curiosity. This suggests implementing the FEP may enhance the
robustness and generalization of RL models, potentially aligning the learning
processes of artificial and biological agents.
