R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models
This addresses the problem of sparse-reward, continuous-action robotic control for AI researchers, though it is incremental as it builds on existing active inference methods.
The paper tackled sparse-reward robotic control from pixels in partially observable environments by introducing prior preference learning and self-revision schedules, resulting in improved performance over state-of-the-art models in cumulative rewards, stability, and success rate.
Although research has produced promising results demonstrating the utility of active inference (AIF) in Markov decision processes (MDPs), there is relatively less work that builds AIF models in the context of environments and problems that take the form of partially observable Markov decision processes (POMDPs). In POMDP scenarios, the agent must infer the unobserved environmental state from raw sensory observations, e.g., pixels in an image. Additionally, less work exists in examining the most difficult form of POMDP-centered control: continuous action space POMDPs under sparse reward signals. In this work, we address issues facing the AIF modeling paradigm by introducing novel prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments. Empirically, we show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate. The code in support of this work can be found at https://github.com/NACLab/robust-active-inference.