However, this is not always the case, as different tasks can compete with each other and lead to weaker models.

Specifically, we extend the Catalyst approach originally designed fordeterministic objectives tothe stochastic setting.

Wefurther propose newtraining methods todisentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies.

We achieve an average task solve rate of 66% compared to a 29% solve rate by GPT -4's intrinsic planning with chain-of-thought prompting.

In the reinforcement learning context, anOption means a temporally extended sequence of actions [30],andisregarded asuseful formanypurposes, such asspeeding uplearning, transferring skills across domains, and solving long-term planning problems.

To combat this, there has been a surge of interest in algorithmic fairness metrics (Mehrabi et al., 2021).

Seeded segmentation in computer vision and graph-based semi-supervised machine learning are essentially thesameproblem.

A recent trend in online algorithms has seen the use of future predictions generated by ML techniques to bypass pessimistic worst-case lower bounds.

A naiveimplementation of this approach leads to the dynamic component taking over the static one as the representation of the former is inherently more general and prone to overfitting.