mlsanityData to 14 Oct 2026

Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms

arXiv 2404.10645 ↗First posted 16 Apr 2024cs.LG cs.AI cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

Distributed Distributional DrQ is a model-free and off-policy RL algorithm for continuous control tasks based on the state and observation of the agent, which is an actor-critic method with the data-augmentation and the distributional perspective of critic value function. Aim to learn to control the agent and master some tasks in a high-dimensional continuous space. DrQ-v2 uses DDPG as the backbone and achieves out-performance in various continuous control tasks. Here Distributed Distributional DrQ uses Distributed Distributional DDPG as the backbone, and this modification aims to achieve better performance in some hard continuous control tasks through the better expression ability of distributional value function and distributed actor policies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 25 of its 42 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

25 of 42 references.

References · 25

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13 more
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