TLDR: A new research paper introduces a novel pre-training method for reinforcement learning (RL) agents using “discounted empowerment.” This information-theoretic measure quantifies an agent’s potential influence over its environment, enabling policies to learn a robust understanding of environmental dynamics without external rewards. The pre-trained policies serve as a highly effective initialization, significantly accelerating data-efficient adaptation to various downstream tasks across different RL algorithms and environments.
A new study introduces a novel approach to pre-training artificial intelligence agents in reinforcement learning (RL), aiming to make them more adaptable and efficient when tackling new tasks. The research, titled “Information-Theoretic Policy Pre-Training with Empowerment,” explores how a concept called “empowerment” can be leveraged to give RL agents a head start, much like foundation models in other AI fields.
Reinforcement learning agents typically learn by trial and error, receiving rewards for desired behaviors. However, in complex environments, these rewards can be sparse or delayed, making the learning process slow and inefficient. Pre-training helps agents acquire foundational knowledge about their environment before being exposed to specific tasks, significantly improving their ability to adapt.
Understanding Empowerment
Empowerment is an information-theoretic measure that quantifies an agent’s potential influence over its environment. Imagine an agent in a room: a highly empowered agent would be one that has many options to change its future state, like opening doors, moving objects, or navigating to different areas. This intrinsic motivation encourages exploration and helps the agent understand the underlying dynamics of its environment without needing any external rewards.
The researchers, Moritz Schneider, Robert Krug, Narunas Vaskevicius, Michael Volpp, Luigi Palmieri, and Joschka Boedecker, extended the traditional idea of empowerment by introducing “discounted empowerment.” While standard empowerment often looks at influence over a fixed number of future steps (n-step empowerment), this can become problematic over very long horizons, as almost any state might become reachable from any starting point, making the signal less useful. Discounted empowerment addresses this by considering a weighted sum of influence over various short and long-term horizons, providing a more robust and flexible measure.
A New Pre-Training Paradigm
The core idea of this research is to use discounted empowerment as a pre-training signal. Instead of training an agent to achieve a specific task, it is initially trained to maximize its empowerment. This means the agent learns to navigate to states where it has the most control and options for future actions. This process helps the agent build a fundamental understanding of how its actions affect the environment.
Once pre-trained, this “empowered” policy serves as a strong initialization for various downstream tasks. When the agent is then introduced to a specific task with its own reward system, it can adapt much more quickly because it already possesses a broad repertoire of behaviors and an implicit understanding of the environment’s structure. This is a significant departure from previous uses of empowerment, which often focused on it as an exploration tool during task learning or for discovering diverse skills, rather than as a general-purpose initialization for a single policy.
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Experimental Insights
The team conducted experiments in both deterministic and stochastic gridworld environments, testing several popular reinforcement learning algorithms, including REINFORCE, Actor-Critic (AC), Proximal Policy Optimization (PPO), and Deep Q-Network (DQN).
Key findings include:
- Agents pre-trained to maximize empowerment consistently outperformed agents trained from scratch, demonstrating improved data efficiency during fine-tuning for new tasks.
- Discounted empowerment proved particularly effective, eliminating the need to carefully tune a specific horizon length for empowerment calculation.
- The benefits of empowerment pre-training were observed across various RL algorithms, suggesting it acts as a valuable mechanism for stabilizing the learning process, similar to variance reduction techniques. While some advanced algorithms like PPO already have strong built-in variance reduction, empowerment still provided complementary advantages. For DQN, it not only accelerated learning but also led to better final performance.
- The approach also proved robust in stochastic environments, where uncertainty makes learning more challenging.
This work paves the way for future research to scale this framework to more complex, high-dimensional tasks, potentially leading to the development of “foundation models” in reinforcement learning that can generalize across a vast array of real-world applications. For more details, you can read the full research paper here.
