Simplifying Deep Temporal Difference Learning (2407.04811v2)

Abstract: Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation like deep neural networks require several additional tricks to stabilise training, primarily a replay buffer and target networks. Unfortunately, the delayed updating of frozen network parameters in the target network harms the sample efficiency and, similarly, the replay buffer introduces memory and implementation overheads. In this paper, we investigate whether it is possible to accelerate and simplify TD training while maintaining its stability. Our key theoretical result demonstrates for the first time that regularisation techniques such as LayerNorm can yield provably convergent TD algorithms without the need for a target network, even with off-policy data. Empirically, we find that online, parallelised sampling enabled by vectorised environments stabilises training without the need of a replay buffer. Motivated by these findings, we propose PQN, our simplified deep online Q-Learning algorithm. Surprisingly, this simple algorithm is competitive with more complex methods like: Rainbow in Atari, R2D2 in Hanabi, QMix in Smax, PPO-RNN in Craftax, and can be up to 50x faster than traditional DQN without sacrificing sample efficiency. In an era where PPO has become the go-to RL algorithm, PQN reestablishes Q-learning as a viable alternative.

Simplifying Deep Temporal Difference Learning

The paper "Simplifying Deep Temporal Difference Learning" by Matteo Gallici et al. proposes a framework for efficient and stable deep reinforcement learning (RL) through Parallelized Q-Network (PQN), an algorithm that leverages LayerNorm and $\ell_2$ regularization to stabilize Temporal Difference (TD) learning without the need for target networks or replay buffers.

Key Insights and Results

The paper outlines a rigorous theoretical analysis confirming the stabilizing properties of LayerNorm and $\ell_2$ regularization in TD methods, addressing two primary sources of instability: off-policy sampling and nonlinear function approximation. By mitigating these instabilities, the authors demonstrate that TD learning can be stabilized even in challenging conditions, with empirical validations provided in both single-agent and multi-agent RL tasks.

Simplified TD Learning through PQN

The core innovation of the paper is PQN. This new algorithm simplifies the deep Q-learning process, eliminating complex mechanisms like target networks and replay buffers. Instead, PQN leverages a parallelized environment that samples data synchronously from multiple environments. This approach maintains computational efficiency and stability while accelerating the training process.

Theoretical Contributions

1. BatchNorm Instability:
   • The paper shows that BatchNorm can lead to myopic behavior in TD methods, particularly for large batch sizes where the BeLLMan operator's expectation converges to the immediate reward rather than considering long-term returns.
2. LayerNorm and $\ell_2$ Regularization:
   • The authors prove that LayerNorm, combined with $\ell_2$ regularization, mitigates both off-policy and nonlinear instability, ensuring the convergence of TD methods. This insight is mathematically formalized through derived bounds.

Empirical Evaluation

PQN is evaluated across a diversity of benchmark environments, demonstrating competitive performance:

1. Atari Learning Environment:
   • In the Atari-10 and full Atari-57 suites, PQN performs competitively against advanced methods like Rainbow and PPO, achieving substantial speedups in training time (up to 50x faster than traditional DQN) without compromising sample efficiency.
2. Open-ended Tasks:
   • In the Craftax environment, a demanding open-ended task, PQN outperforms PPO in terms of both final scores and sample efficiency, validating its robustness and generalizability.
3. Multi-Agent Environments:
   • PQN achieves state-of-the-art performance in multi-agent RL scenarios like SMAC and Hanabi while simplifying the training pipeline by avoiding distributed RL complexities, maintaining high computational efficiency.

Practical and Theoretical Implications

The primary practical implication of PQN is its ability to run entirely on GPU, paving the way for a new generation of efficient RL methods. This aligns with recent trends toward deep vectorized RL (DVRL), indicating PQN's robustness in handling computational tasks involving large-scale, parallel environments.

Theoretically, the paper sets a foundation for understanding how regularization techniques stabilize TD learning. LayerNorm, in particular, emerges as a crucial component in the design of stable function approximators for RL methods.

Future Directions

Future research could explore further optimizations of PQN, including advanced exploration strategies beyond $\epsilon$-greedy policies to enhance performance in hard-exploration domains. Additional studies could also investigate the combination of PQN with other forms of regularization and normalization to further boost stability and performance in diverse RL settings.

In sum, this work reestablishes the viability of Q-learning in modern RL frameworks by addressing core stability issues and simplifying the algorithmic pipeline, providing a promising direction for future AI developments.