Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers (2411.15370v2)

Abstract: Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or expensive batch updates, or both, making them incompatible for real systems with resource-limited computers. We show that these methods fail catastrophically when limited to small replay buffers or during incremental learning, where updates only use the most recent sample without batch updates or a replay buffer. We propose a novel incremental deep policy gradient method -- Action Value Gradient (AVG) and a set of normalization and scaling techniques to address the challenges of instability in incremental learning. On robotic simulation benchmarks, we show that AVG is the only incremental method that learns effectively, often achieving final performance comparable to batch policy gradient methods. This advancement enabled us to show for the first time effective deep reinforcement learning with real robots using only incremental updates, employing a robotic manipulator and a mobile robot.

• The paper introduces a novel incremental method, AVG, that replaces batch updates, target networks, and replay buffers for deep policy gradient learning.
• It employs innovative normalization and scaling techniques to manage unstable gradient updates in resource-constrained environments.
• Empirical results show AVG’s competitive performance on benchmarks, achieving successful real-time deep RL on simulations and physical robots.

Overview of "Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers"

The paper, "Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers," presents a significant advancement in the field of deep reinforcement learning (RL), specifically targeted at applications with resource constraints such as robotic systems. The authors introduce a novel algorithm, Action Value Gradient (AVG), which operates incrementally—eschewing traditional components such as large replay buffers, target networks, and batch updates that typically bolster the stability and performance of deep policy gradient methods.

Key Contributions

1. Incremental Learning Method: AVG is an incremental deep policy gradient method that leverages the reparameterization gradient estimator and incorporates a set of normalization and scaling techniques to maintain stability without relying on extensive computational resources. This approach is particularly well-suited for learning in constrained environments, such as robotics, where only recent samples are available for updates.
2. Effective Use of Normalization and Scaling: The paper identifies that stabilization in incremental methods is achieved through observation normalization, penultimate normalization of neural network layers, and scaling of temporal difference (TD) errors. These techniques effectively manage the large gradient updates that inherently occur in incremental learning without replay buffers.
3. Empirical Validation: AVG's efficacy is demonstrated through impressive results on a suite of robotic simulation benchmarks where it matches or even surpasses traditional batch policy gradient methods like SAC when constrained by limited resources. Moreover, the method is the first to showcase successful real-time deep RL on physical robots using only incremental updates, marking a significant achievement in the field.

Strong Numerical Results

In rigorous evaluations across multiple complex control tasks, including familiar benchmarks like MuJoCo environments and the DeepMind Control Suite, AVG consistently shows state-of-the-art performance among incremental methods. It maintains competitive performance levels compared to high-resource batch methods, such as SAC, while operating under significantly constrained conditions (i.e., single experience updates without replay buffers).

Practical and Theoretical Implications

Practically, AVG brings deep RL closer to being deployable in real-world scenarios where computational and storage resources are limited. This advancement is crucial for fields like autonomous robotic systems, where on-the-fly adaptation and learning in dynamic environments can transform operational capabilities—such as enabling continuous learning directly onboard edge devices and robots. Theoretically, the paper extends the understanding of how reparameterization gradients can be effectively utilized in an incremental learning context with stability, shaping future research in stable policy optimization under constraints.

Future Directions

The work invites further investigation into enhancing AVG's sample efficiency, possibly by integrating eligibility traces or exploring stability across hyperparameter choices. Moreover, extending its application to discrete action spaces and other domains, such as natural language processing or vision-based tasks, presents an exciting avenue for future research.

In summary, the authors provide not just a methodological contribution with AVG, but also the theoretical innovations and insights necessary to push forward the use of deep reinforcement learning methods in scenarios where traditional batch processing is infeasible. This pioneering work lays the groundwork for more adaptive, resource-efficient intelligent systems capable of operating in real-time environments.