Summary of Broad Critic Deep Actor Reinforcement Learning For Continuous Control, by Shiron Thalagala et al.
Broad Critic Deep Actor Reinforcement Learning for Continuous Control
by Shiron Thalagala, Pak Kin Wong, Xiaozheng Wang
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel hybrid architecture for actor-critic reinforcement learning (RL) algorithms integrates broad learning system (BLS) and deep neural networks (DNNs). This approach optimizes critic network parameters using ridge regression and actor network parameters through gradient descent. The proposed algorithm outperforms DDPG in terms of computational efficiency and accelerated learning trajectory, as evaluated on two classic continuous control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new architecture for deep reinforcement learning (DRL) that combines the strengths of broad learning systems (BLS) and deep neural networks (DNNs). This hybrid approach uses ridge regression to estimate critic network parameters and gradient descent to optimize actor network parameters. The results show that this algorithm is better than DDPG in terms of efficiency and learning speed. |
Keywords
» Artificial intelligence » Gradient descent » Regression » Reinforcement learning
