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Found 7 papers, 2 papers with code
Intelligent problem-solving as integrated hierarchical reinforcement learning
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Developing hierarchical anticipations via neural network-based event segmentation
Humans can make predictions on various time scales and hierarchical levels.
Inference of Affordances and Active Motor Control in Simulated Agents
We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps.
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hidden, task-relevant factors.
Latent Event-Predictive Encodings through Counterfactual Regularization
A critical challenge for any intelligent system is to infer structure from continuous data streams.
Hierarchical principles of embodied reinforcement learning: A review
We then relate these insights with contemporary hierarchical reinforcement learning methods, and identify the key machine intelligence approaches that realise these mechanisms.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Here, we introduce a computational learning architecture, termed surprise-based behavioral modularization into event-predictive structures (SUBMODES), that explores behavior and identifies the underlying behavioral units completely from scratch.
