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For SALE: State-Action Representation Learning for Deep Reinforcement Learning

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arxiv 2306.02451 v2 pith:TDM4CSZ2 submitted 2023-06-04 cs.LG cs.AIstat.ML

Scott Fujimoto , Wei-Di Chang , Edward J. Smith , Shixiang Shane Gu , Doina Precup , David Meger This is my paper
classification cs.LGcs.AIstat.ML
keywords learningrepresentationsalecontroldesignembeddingslow-levelreinforcement
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In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276.7% and 50.7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.

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Cited by 3 Pith papers

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    Ms.PR applies multi-scale predictive supervision to enforce goal-directed alignment in latent spaces for offline GCRL, yielding improved representation quality and performance on vision and state-based tasks.

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    FlashSAC scales up Soft Actor-Critic with fewer updates, larger models, higher data throughput, and norm bounds to deliver faster, more stable training than PPO on high-dimensional robot control tasks across dozens of...

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    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.

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