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Simplifying Deep Temporal Difference Learning
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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 large replay buffer and target networks. Unfortunately, the delayed updating of frozen network parameters in the target network harms the sample efficiency and, similarly, the large replay buffer introduces memory and implementation overheads. In this paper, we investigate whether it is possible to accelerate and simplify off-policy 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 or replay buffer, even with off-policy data. Empirically, we find that online, parallelised sampling enabled by vectorised environments stabilises training without the need for a large 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, PPO-RNN in Craftax, QMix in Smax, 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 off-policy Q-learning as a viable alternative.
Forward citations
Cited by 6 Pith papers
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Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning
Replacing the hard target-copy in DQN with a Q-value-sensitivity-weighted merge of the last K network copies yields competitive Atari performance, but only marginally beats architecture-matched baselines.
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TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
TABX is a JAX-based, GPU-accelerated, configurable multi-agent battle simulator that lets researchers vary units, terrain, and physics to benchmark cooperative MARL algorithms.
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Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
The paper introduces MTBench, a GPU-accelerated benchmark for massively parallel multi-task RL, and reports experiments suggesting on-policy methods outperform off-policy baselines while value learning limits MTRL per...
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Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
OIR relabels failed trajectories via an LLM into open-ended instructions and trains a unified instruction-following policy, outperforming PQN and ELLM on Craftax.
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Priors Matter: Addressing Misspecification in Bayesian Deep Q-Learning
Bayesian deep Q-learning exhibits a cold posterior effect, caused partly by misspecified Gaussian priors, and Laplace or meta-learned priors improve performance.
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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies
A survey that categorizes deep reinforcement learning scaling strategies into data, network, and training budget dimensions and outlines challenges for scaling DRL systems.
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