The Primacy Bias in Deep Reinforcement Learning (2205.07802v1)

Abstract: This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.

• The paper identifies primacy bias in deep RL, showing that over-reliance on initial experiences degrades long-term agent performance.
• It introduces a simple reset mechanism for the neural network's later layers while preserving past interactions in a replay buffer.
• Experimental results across Atari and DeepMind benchmarks confirm that resets effectively mitigate overfitting and enhance sample efficiency.

The Primacy Bias in Deep Reinforcement Learning

The paper "The Primacy Bias in Deep Reinforcement Learning" investigates a fundamental issue observed in deep reinforcement learning (RL) systems: the tendency to over-rely on initial experiences, causing degradation in performance when encountering later, potentially more informative experiences. This phenomenon is identified as the "primacy bias." This bias reflects a notable challenge within deep RL, as early overfitting could compromise the long-term effectiveness of an RL agent’s learning process.

The authors undertake a thorough investigation of how deep RL algorithms exhibit symptomatic susceptibilities akin to the primacy bias, which is a concept inspired by studies from cognitive science. This bias implies that positive or negative reinforcement derived from initial interactions disproportionately influences subsequent learning phases, often preventing the RL agent from optimally exploiting its gathered experience.

To counteract the primacy bias, the authors propose a simple yet effective strategy: periodically resetting parts of the agent’s neural network, specifically the last layers, while maintaining the history of experiences in a replay buffer. The replay buffer serves as a persistent storage for all interactions experienced by the agent, avoiding the need to completely restart learning from scratch. This technique, tested across various benchmarks, significantly enhances performance without introducing additional computational complexity.

The experimental undertaking spanned both discrete action scenarios, specifically the Atari 100k benchmark, and continuous action domains represented by the DeepMind Control Suite. The resetting mechanism was demonstrated to be effective across several algorithms and input domains, including SPR (Self-Predictive Representations), SAC (Soft Actor-Critic), and DrQ (Data-efficient Rainbow Q-learning with Pixel Observations), improving their robustness against the primacy bias.

Numerical Results and Observations

The experimental results prominently highlight that resets allow deep RL agents to achieve significant performance improvements:

1. Performance Recovery and Enhancement:
   • Resets enable agents to recover quickly post-reset, utilizing the reserve of knowledge in the replay buffer. This process results in qualitative elevation of agent capability, demonstrated by achieving near-optimal performance in tasks previously hindered by early overfitting.
2. Resistance Against Overfitting:
   • Agents were more resilient to overfitting with resets applied, which facilitated better data reuse and reduced the compounding negative effects of initial suboptimal experiences on algorithm performance.
3. Facilitating Robust Learning Dynamics:
   • The insights provided by analyzing the interactions between resetting, replay ratios, and $n$-step targets revealed that higher replay ratios benefit significantly from resets, illustrating a mitigated risk of primacy bias under these conditions.
4. Hyperparameter Landscape:
   • The efficacy of the reset mechanism revealed a reshaped hyperparameter landscape, unlocking potentially superior configurations that were not feasible before due to early overfitting concerns.

Implications and Speculation on AI Developments

The insights gained from this paper have both theoretical and practical ramifications for future AI systems executing tasks in dynamic and evolving environments. The capacity to mitigate primacy bias through a judicious resetting mechanism paves the way for more robust and adaptable AI agents, capable of learning effectively from large streams of data.

From a practical perspective, integrating resetting strategies in deep RL pipelines can facilitate enhanced adaptability and error recovery without incurring additional computational costs. This advantage opens opportunities for deploying deep RL solutions in resource-constrained or data-sparse environments where maximizing sample efficiency is crucial.

Theoretically, acknowledging the impact of the primacy bias and the utility of resets encourages further exploration of cognitive-inspired adjustments in artificial neural networks for learning. Such exploration can lead to architectures and algorithms that parallel human adaptability and learning efficiency.

The paper underlines the importance of a holistic approach in RL research, suggesting that significant advancements can derive from exploring synergies between reinforcement and deep learning methodologies, analogous to successful strategies like Batch Normalization in supervised learning. This paper of the primacy bias in deep RL offers a pivotal step toward understanding and optimizing the nuanced behaviors of learning dynamics involving deep neural networks.