Summary of Learning Diverse Policies with Soft Self-generated Guidance, by Guojian Wang et al.
Learning Diverse Policies with Soft Self-Generated Guidance
by Guojian Wang, Faguo Wu, Xiao Zhang, Jianxiang Liu
First submitted to arxiv on: 7 Feb 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 This research paper proposes a novel approach to reinforcement learning (RL) in scenarios where rewards are sparse or deceptive. The challenge lies in calculating the gradient of the agent’s policy, as non-zero rewards are rarely obtained, resulting in stochastic gradients without valid information. To overcome this limitation, the authors draw upon memory buffers of previous experiences to facilitate faster and more efficient online RL. However, existing methods often require successful experiences, leading to suboptimal behaviors. The proposed algorithm combines a policy improvement step with an exploration step using offline demonstration data, leveraging diverse past trajectories as guidance rather than imitation. This innovative approach enables the agent to learn without rewards and approach optimality. Furthermore, a novel diversity measurement is introduced to maintain team diversity and regulate exploration. Experimental results on discrete and continuous control tasks demonstrate significant improvements over existing RL methods in terms of diverse exploration and avoiding local optima. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can learn from experience even when they don’t always get rewarded. Imagine you’re playing a game where you sometimes get points, but mostly not. It’s hard to figure out what works because the rewards are so rare. Researchers have found that remembering past experiences can help with this problem. However, most methods require those experiences to be successful and might even exploit them too much. This paper proposes a new way of using past experiences as guidance instead of copying them exactly. This allows the machine to learn without rewards and do better in the long run. The authors also introduce a new measure to make sure the machine explores different options and doesn’t get stuck. |
Keywords
* Artificial intelligence * Reinforcement learning
