Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability (2107.06277v1)

Abstract: Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL problem necessitates new approaches to generalization beyond the well-studied techniques used in supervised learning. While supervised learning methods can generalize effectively without explicitly accounting for epistemic uncertainty, we show that, perhaps surprisingly, this is not the case in RL. We show that generalization to unseen test conditions from a limited number of training conditions induces implicit partial observability, effectively turning even fully-observed MDPs into POMDPs. Informed by this observation, we recast the problem of generalization in RL as solving the induced partially observed Markov decision process, which we call the epistemic POMDP. We demonstrate the failure modes of algorithms that do not appropriately handle this partial observability, and suggest a simple ensemble-based technique for approximately solving the partially observed problem. Empirically, we demonstrate that our simple algorithm derived from the epistemic POMDP achieves significant gains in generalization over current methods on the Procgen benchmark suite.

• The paper introduces the epistemic POMDP concept to capture uncertainty by transforming fully observed MDPs into test-time POMDPs.
• It presents LEEP, an ensemble-based algorithm that significantly enhances generalization performance over standard methods like PPO.
• It reveals that optimal test-time policies must be non-Markovian and uncertainty-aware, challenging traditional deterministic approaches.

The Challenge of Generalization in Reinforcement Learning through the Lens of Epistemic POMDPs

The paper "Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability" explores the central issue of generalization in reinforcement learning (RL), proposing a novel way to address it using epistemic partially observed Markov decision processes (POMDPs). Unlike supervised learning, where generalization from a training set to unseen data is relatively straightforward, RL presents unique challenges due to its sequential decision-making structure and the intrinsic variability of environmental dynamics and rewards. This paper argues that these challenges necessitate distinct approaches, underscoring the insufficiency of empirical risk minimization techniques commonly employed in supervised contexts.

Key Concepts

The core innovation in this paper is the concept of the epistemic POMDP, which reimagines an RL problem in the context of broader uncertainties. The paper posits that when generalizing from a limited set of training contexts to new test-time environments, the agent's incomplete knowledge induces a form of partial observability, even in environments that are fully observable under standard conditions. This partial observability is tied to epistemic uncertainty, transforming fully observed MDPs into POMDPs at test time. The epistemic POMDP models the agent's uncertainty about the identity of the environment during test time as a distribution over possible environments consistent with the observed training conditions.

Empirical Findings and Numerical Results

The authors empirically demonstrate the implications of these concepts through methodical experiments, particularly on the Procgen benchmark suite. A significant empirical contribution of the paper is their algorithm, LEEP (Linked Ensembles for the Epistemic POMDP), which achieves marked improvements in generalization performance. By leveraging ensemble-based methodologies, LEEP navigates the epistemic uncertainties better than traditional RL methods like PPO, showcasing superior performance across tasks with notable generalization gaps, such as Maze and Heist.

Implications and Theoretical Contributions

The theoretical insights provided by the paper are far-reaching. In particular, the paper's assertion that the Bayes-optimal policy for test-time performance is inherently non-Markovian and, when restricted to memoryless policies, stochastic, suggests significant yet seldom-considered implications for policy learning. The policy's adaptivity — altering behavior based not only on observed rewards but also on inferred uncertainties — challenges the prevalent reliance on deterministic and Markovian policies and underscores the importance of accounting for uncertainty explicitly.

Moreover, the findings caution against the oversimplification of using maximum-entropy or uniform stochastic strategies as blanket recommendations without regard to the specific uncertainties present, as these approaches can result in considerable suboptimality relative to a well-calibrated epistemic approach.

Future Directions

The paper opens several avenues for future research. Developing scalable algorithms that adeptly approximate the epistemic POMDP remains a primary challenge. This requires enhanced techniques for model uncertainty estimation and belief state inference that can function efficiently in high-dimensional observation spaces typical to RL environments. Furthermore, the adaptation of POMDP-solving algorithms to leverage the insights revealed through this research might bridge the gap between theoretical optimality and practical applicability in RL paradigms.

Conclusion

The work by Dibya Ghosh and colleagues offers a critical re-examination of generalization in reinforcement learning, emphasizing the nuances introduced by epistemic uncertainties which transform MDPs into POMDP-like problems at a conceptual level. The implications of this work suggest a pivot in how the AI community approaches RL generalization issues, advocating for an increased subtlety in policy learning that adapts not only to empirical data but also to manifold uncertainties inherent in unseen, future contexts.