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Found 10 papers, 5 papers with code
Controllable Neural Symbolic Regression
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants.
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data.
End-to-end symbolic regression with transformers
Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the "skeleton" of the expression up to the choice of numerical constants, then fitting the constants by optimizing a non-convex loss function.
Deep Symbolic Regression for Recurrent Sequences
Symbolic regression, i. e. predicting a function from the observation of its values, is well-known to be a challenging task.
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Learning meaningful behaviors in the absence of reward is a difficult problem in reinforcement learning.
SaLinA: Sequential Learning of Agents
SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms.
Meta-Reinforcement Learning With Informed Policy Regularization
Meta-reinforcement learning aims at finding a policy able to generalize to new environments.
Privileged Information Dropout in Reinforcement Learning
Using privileged information during training can improve the sample efficiency and performance of machine learning systems.
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization
We test the performance of our algorithm in a variety of environments where tasks may vary within each episode.
FACMAC: Factored Multi-Agent Centralised Policy Gradients
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
