Summary of Soft Actor-critic with Beta Policy Via Implicit Reparameterization Gradients, by Luca Della Libera
Soft Actor-Critic with Beta Policy via Implicit Reparameterization Gradients
by Luca Della Libera
First submitted to arxiv on: 8 Sep 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 recent paper investigates the application of soft actor-critic (SAC) reinforcement learning in complex tasks, focusing on improving sample efficiency. SAC combines stochastic policy optimization and off-policy learning, but its applicability is limited to distributions whose gradients can be computed through reparameterization trick. The authors explore implicit reparameterization, a technique that extends the class of reparameterizable distributions. They use this method to train SAC with the beta policy on simulated robot locomotion environments and compare its performance with common baselines. Experimental results show that the beta policy outperforms the normal policy and is comparable to the squashed normal policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in deep reinforcement learning called poor sample efficiency, which makes it hard to use these powerful algorithms in real-world situations. The researchers propose using a new way of calculating gradients, called implicit reparameterization, to make SAC work with more types of distributions. They test this idea on robots and show that it works just as well as other methods. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
