Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

In reinforcement learning (RL), a reward function that aligns exactly with a task's true
performance metric is often necessarily sparse. For example, a true task metric might …

Many reinforcement learning environments (eg Minecraft) provide only sparse rewards that
indicate task completion or failure with binary values. The challenge in exploration efficiency …

Preference-based reinforcement learning (RL) provides a framework to train agents using
human preferences between two behaviors. However, preference-based RL has been …

We propose a method for meta-learning reinforcement learning algorithms by searching
over the space of computational graphs which compute the loss function for a value-based …

Reinforcement Learning and recently Deep Reinforcement Learning are popular methods
for solving sequential decision making problems modeled as Markov Decision Processes …

Reinforcement learning from human feedback (RLHF) alleviates the problem of designing a
task-specific reward function in reinforcement learning by learning it from human preference …

We present a novel procedure for optimization based on the combination of efficient
quantized tensor train representation and a generalized maximum matrix volume principle …

Vizier is the de-facto blackbox optimization service across Google, having optimized some of
Google's largest products and research efforts. To operate at the scale of tuning thousands …