Overleaf Example
–Neural Information Processing Systems
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions--parts of the episode where the latent state is fixed--and propose three key modifications to existing meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) prior latent conditioning. We demonstrate the importance of these modifications in various domains, ranging from discrete Gridworld environments to continuouscontrol and simulated robot assistive tasks, illustrating the efficacy of DynaMITE-RL over state-of-the-art baselines in both online and offline RL settings.
Neural Information Processing Systems
May-25-2025, 21:48:13 GMT
- Country:
- North America > United States
- California (0.14)
- Massachusetts (0.14)
- North America > United States
- Genre:
- Research Report > Experimental Study (1.00)
- Industry:
- Education > Educational Setting (0.67)
- Leisure & Entertainment (0.93)
- Media > Television (0.46)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning
- Learning Graphical Models (1.00)
- Neural Networks > Deep Learning (0.67)
- Reinforcement Learning (1.00)
- Natural Language (1.00)
- Representation & Reasoning (1.00)
- Robots (1.00)
- Machine Learning
- Information Technology > Artificial Intelligence