Visualizing the World Model in an Agent's Value Function
cs.AI, cs.LG
Submitted: 2026-10-09
Updated: 2026-10-09
Terminology
Sources
- Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
- Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
- Cognitive Mapping and Planning for Visual Navigation
- World Models
- Benchmarking the Spectrum of Agent Capabilities
- Dream to Control: Learning Behaviors by Latent Imagination
- Adam: A Method for Stochastic Optimization
- Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
- Counterfactual Credit Assignment in Model-Free Reinforcement Learning
- Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
- Neural Map: Structured Memory for Deep Reinforcement Learning
- On the difficulty of training Recurrent Neural Networks
- Proximal Policy Optimization Algorithms
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems