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Found 20 papers, 6 papers with code
Neural Networks and the Chomsky Hierarchy
Reliable generalization lies at the heart of safe ML and AI.
Model-Free Risk-Sensitive Reinforcement Learning
Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains.
Causal Analysis of Agent Behavior for AI Safety
As machine learning systems become more powerful they also become increasingly unpredictable and opaque.
Algorithms for Causal Reasoning in Probability Trees
Probability trees are one of the simplest models of causal generative processes.
Meta-trained agents implement Bayes-optimal agents
Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.
Action and Perception as Divergence Minimization
While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences.
Meta reinforcement learning as task inference
This includes proposals to learn the learning algorithm itself, an idea also known as meta learning.
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings
Modeling the agent-environment interaction using causal influence diagrams, we can answer two fundamental questions about an agent's incentives directly from the graph: (1) which nodes can the agent have an incentivize to observe, and (2) which nodes can the agent have an incentivize to control?
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.
AI Safety Gridworlds
We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
Human Decision-Making under Limited Time
Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i. e. decision-makers are bounded in their rationality.
Memory shapes time perception and intertemporal choices
There is a consensus that human and non-human subjects experience temporal distortions in many stages of their perceptual and decision-making systems.
Information-Theoretic Bounded Rationality
Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics.
Belief Flows of Robust Online Learning
This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function.
Subjectivity, Bayesianism, and Causality
Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty.
An Adversarial Interpretation of Information-Theoretic Bounded Rationality
Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary.
