mlsanityData to 14 Oct 2026

Learning Agents With Prioritization and Parameter Noise in Continuous State and Action Space

arXiv 2410.11250 ↗First posted 15 Oct 2024cs.AI cs.LGcited by 0

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

Abstract (arXiv)

Among the many variants of RL, an important class of problems is where the state and action spaces are continuous -- autonomous robots, autonomous vehicles, optimal control are all examples of such problems that can lend themselves naturally to reinforcement based algorithms, and have continuous state and action spaces. In this paper, we introduce a prioritized form of a combination of state-of-the-art approaches such as Deep Q-learning (DQN) and Deep Deterministic Policy Gradient (DDPG) to outperform the earlier results for continuous state and action space problems. Our experiments also involve the use of parameter noise during training resulting in more robust deep RL models outperforming the earlier results significantly. We believe these results are a valuable addition for continuous state and action space problems.

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 7 of its 18 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

7 of 18 references.

References · 7

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2015Prioritized Experience Replaycited by 1,099
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  6. 2017Deep Reinforcement Learning: An Overviewcited by 333
  7. 2017Parameter Space Noise for Explorationcited by 161

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  1. 2024Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
  2. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  3. 2024Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm
  4. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
  5. 2025Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
  6. 2025Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network
  7. 2025Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
  8. 2026Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning