Agents Trained through Reinforcement Learning Exhibit Human-Like Decision-Making Flexibility
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View abstract on PubMed
Summary
This summary is machine-generated.Reinforcement learning (RL) agents demonstrated superior decision-making flexibility compared to supervised learning (SL) agents in a cognitive task. RL effectively models human adaptability in AI.
Area Of Science
- Cognitive Science
- Artificial Intelligence
- Computational Neuroscience
Background
- Decision-making flexibility is crucial for human cognition and adapting to changing environments.
- Artificial intelligence (AI) agents, particularly those using deep neural networks, are used to simulate human cognitive processes.
- Both supervised learning (SL) and reinforcement learning (RL) are employed to train AI agents, but their efficacy in replicating human decision-making flexibility is not fully understood.
Purpose Of The Study
- To compare the effectiveness of supervised learning (SL) and reinforcement learning (RL) in training AI agents with human-like decision-making flexibility.
- To investigate how different learning paradigms influence an agent's ability to adapt strategies under varying decision criteria.
Main Methods
- Identical deep artificial neural network architectures were trained using both SL and RL paradigms.
- Agents were tasked with a memory-based decision task under three distinct criteria: precise, conservative, and liberal.
- Performance was evaluated based on the agents' ability to adapt their decision-making strategies.
Main Results
- Agents trained with both SL and RL performed accurately under the precise decision criterion.
- Only RL-trained agents successfully adapted to the conservative and liberal decision criteria, demonstrating superior flexibility.
- RL-trained agents significantly outperformed SL-trained agents in adapting to diverse decision-making scenarios.
Conclusions
- Reinforcement learning (RL) is a more effective paradigm than supervised learning (SL) for modeling human-like decision-making flexibility in AI agents.
- These findings offer valuable insights for developing AI systems that better replicate human cognitive functions for real-world applications.
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