Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning (2006.01096v3)

Abstract: A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training. In this paper, we approach this challenge through the following invariance principle: an agent must find a representation such that there exists an action-predictor built on top of this representation that is simultaneously optimal across all training domains. Intuitively, the resulting invariant policy enhances generalization by finding causes of successful actions. We propose a novel learning algorithm, Invariant Policy Optimization (IPO), that implements this principle and learns an invariant policy during training. We compare our approach with standard policy gradient methods and demonstrate significant improvements in generalization performance on unseen domains for linear quadratic regulator and grid-world problems, and an example where a robot must learn to open doors with varying physical properties.

Invariant Policy Optimization: Enhancing Generalization in Reinforcement Learning

The paper introduces Invariant Policy Optimization (IPO), a novel algorithm aimed at improving the generalization capabilities of reinforcement learning (RL) algorithms across domains not encountered during training. This research addresses a prevalent issue in RL: the tendency of agents to overfit to their training environments, leading to subpar performance in novel scenarios. An improved generalization is particularly crucial for deploying RL in real-world applications, such as robotics, where agents must operate in diverse and unpredictable conditions.

The central tenet of IPO is its focus on learning invariant policies through a causal lens. The hypothesis is that generalization improves when policies leverage invariances stemming from causal relationships present across multiple domains. To achieve this, IPO employs a representation that remains invariant while still ensuring optimal action prediction across all training domains. This strategy seeks to filter out spurious factors that might correlate with successful actions in specific training environments but do not generalize to new contexts.

IPO is inspired by principles of causal inference and draws from methodologies like Invariant Risk Minimization (IRM) and game-theoretic approaches to invariance. The algorithmic framework is structured such that each domain is conceptualized as an intervention within a causal graphical model, promoting the learning of action causes that are invariant across these interventions.

The authors detail the formulation of IPO through a bi-level optimization process, where the goal is to select representations that maximize expected rewards across diverse domains, subject to invariance constraints. This approach is evaluated against traditional policy gradient methods, such as PPO, to highlight its generalization advantages.

The experimental evaluation includes three diverse examples, each demonstrating specific facets of the algorithm's performance:

1. Linear Quadratic Regulator with Distractors: In a classical LQR setting augmented with high-dimensional distractor observations, IPO showed superior performance in filtering irrelevant sensory data, significantly outperforming standard gradient descent and overparameterization approaches. The results highlighted IPO's ability to eschew memorization of non-essential features, aligning with its theoretical foundation.
2. Colored-Key Domains: Within grid-world environments where agents learn to navigate using keys of varying colors, IPO exhibited robust generalization to unseen key colors during testing. Compared to PPO, IPO not only achieved higher average rewards in new domains but also demonstrated consistency across multiple seeds, emphasizing its effectiveness in improving policy robustness.
3. DoorGym Environments: The task involved a robot learning to open doors with varying physical properties. Here, IPO not only maintained higher success rates in environments with different hinge frictions but also noted a qualitative shift in the learned policies. It favored a more robust hooking strategy across all training instances, a testament to its invariant policy framework.

The results corroborate the hypothesis by demonstrating that IPO significantly enhances generalization by focusing on the invariance of causal relationships within the policy decision-making process. The algorithm's potential applications span various practical settings, especially in domains necessitating reliable transfer of learned policies, such as sim-to-real transfers in robotics.

Future directions proposed in the paper include the pursuit of theoretical guarantees for generalization to unseen domains through frameworks like PAC-Bayes, and the integration of IPO with domain randomization techniques to generate diverse and robust training datasets.

In summary, this research contributes to the RL field by introducing a well-founded, theoretically motivated approach to tackling the generalization problem, with promising practical implications. It sets a precedent for leveraging causal inference principles to enrich RL algorithms, paving the way for future innovations in robust policy learning.