partial observability
A situation in reinforcement learning and AI where the agent does not have complete information about the environment or states, posing challenges in decision-making and requiring strategies to handle uncertainty.
- ActiveVOO: Value of Observation Guided Active Knowledge Acquisition for Open-World Embodied Lifted Regression Planning
- Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation
- Forecasting in Offline Reinforcement Learning for Non-stationary Environments
- On Evaluating Policies for Robust POMDPs
- Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
- Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
- Quantifying Generalisation in Imitation Learning
- Real-World Reinforcement Learning of Active Perception Behaviors
- Seeing through Uncertainty: Robust Task-Oriented Optimization in Visual Navigation
- Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
- To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable RL
- VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents