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Found 10 papers, 4 papers with code
SMX: Sequential Monte Carlo Planning for Expert Iteration
SMX demonstrates a statistically significant improvement in performance compared to AlphaZero, as well as demonstrating its performance as an improvement operator for a model-free policy, matching or exceeding top model-free methods across both continuous and discrete environments.
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs.
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
DITTO: Offline Imitation Learning with World Models
We propose DITTO, an offline imitation learning algorithm which uses world models and on-policy reinforcement learning to addresses the problem of covariate shift, without access to an oracle or any additional online interactions.
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery
In this work, we augment small human medical datasets with in-vitro data and animal models.
Planning for Risk-Aversion and Expected Value in MDPs
This motivates us to propose a lexicographic approach which minimises the expected cost subject to the constraint that the CVaR of the total cost is optimal.
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference
Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning.
Active Inference for Integrated State-Estimation, Control, and Learning
This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators.
Robotics
Towards better healthcare: What could and should be automated?
While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field.
Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
This is achieved by first learning a set of visual {`}concepts{'} that abstract the visual feature spaces into concepts that have human-level meaning.
