Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning (2101.05265v2)

Abstract: Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which rarely exploit this structure explicitly. Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states. PSM assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. We also present a contrastive representation learning procedure to embed any state similarity metric, which we instantiate with PSM to obtain policy similarity embeddings (PSEs). We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite.

• The paper presents a novel policy similarity metric and corresponding embeddings that improve RL agent generalization across diverse tasks.
• The approach employs a contrastive learning framework to encode invariances in sequential decision-making via behavioral similarities.
• Empirical evaluations on challenges like jumping tasks and LQR benchmarks demonstrate significant performance gains over traditional methods.

Essay on "Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning"

The paper "Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning" proposes a novel approach to address the lack of generalization in Reinforcement Learning (RL) policies, particularly when agents are trained on a limited number of environments. The authors introduce a policy similarity metric (PSM) and associated policy similarity embeddings (PSEs) to improve an RL agent's ability to generalize across different tasks and environments.

The motivation for this research is grounded in the observation that reinforcement learning agents often fail to generalize well from a set of training environments to unseen ones. Previous methods adapted from supervised learning, such as data augmentation and regularization, do not explicitly take into account the sequential nature of decision-making inherent in RL tasks. This paper advocates for the integration of this sequential structure into the representation learning process.

Theoretical Framework

Central to this research is the development of the Policy Similarity Metric (PSM). The PSM is formally defined to measure the behavioral similarity between states based on optimal policy actions both in the current and future states. This metric is grounded in the concept of bisimulation metrics but diverges by being reward-agnostic, focusing instead on long-term policy behavior similarities. This design choice makes the PSM more robust in generalization scenarios where different environments might have varying reward functions.

The paper demonstrates PSM's theoretical strength by proving that it provides an upper bound on the suboptimality of policies when transferred between environments. This result highlights PSM's superior capacity for ensuring that transferred policies remain effective across different settings, a property not guaranteed by traditional bisimulation metrics.

Contrastive Representation Learning

The authors employ a contrastive learning framework to utilize the PSM for generating what they call Policy Similarity Embeddings (PSEs). This contrastive procedure aligns states with similar PSM values in the embedding space, which encourages generalization by placing behaviorally similar states from different environments close to each other in the learned representation space. Thus, PSEs effectively encode invariances that reflect similarities in optimal behavior across tasks.

Empirical Evaluation

The empirical analysis validates the practical applicability of PSEs across several challenging benchmarks. For example, the jumping task from pixels, linear-quadratic regulators (LQR) with spurious correlations, and the Distracting DM Control Suite are used to benchmark the relative performance improvements offered by PSEs.

In the jumping task example, the paper highlights how PSEs enhance generalization capabilities even with limited training examples. The tasks, which involve varied physical configurations, show that PSEs considerably outperform standard regularization methods and data augmentation techniques, especially when these techniques fail to exploit task-dependent invariances.

In the LQR domain, the use of PSEs enables the model to ignore distractor features, a common problem seen with spurious correlations. The results indicate that the policy trained with PSM-based state aggregation avoids the pitfalls of relying on misleading features, unlike other contemporary generalization approaches.

Scalability and Future Directions

The paper further explores the scalability of PSEs, applying them to the Distracting Control Suite with continuous action spaces, showing their feasible integration with state-of-the-art augmentation methods like DrQ. This compatibility with existing techniques suggests that PSEs can be effectively utilized in conjunction with other methods to enhance performance further.

The research opens avenues for future work in several exciting directions. For instance, extending the PSM to a wider array of distance metrics, applying these concepts in online settings, and exploring the integration of other self-supervised learning techniques are potential explorations that could further advance generalization in RL.

In conclusion, the paper provides a substantial advancement in RL generalization research by leveraging theoretically solid and practically viable methods to embed behavioral similarities across tasks. Its integration of sequential decision-making properties into the learning process stands as a significant contribution, promising broader applicability in more varied RL scenarios.