Search Results for author:
Found 18 papers, 10 papers with code
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents.
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan.
Subgoal Search For Complex Reasoning Tasks
In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework.
Continuous Control With Ensemble Deep Deterministic Policy Gradients
The growth of deep reinforcement learning (RL) has brought multiple exciting tools and methods to the field.
GUIDE: Guidance-based Incremental Learning with Diffusion Models
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten.
Uncertainty-sensitive Learning and Planning with Ensembles
The former manifests itself through the use of value function, while the latter is powered by a tree search planner.
Structure and randomness in planning and reinforcement learning
Planning in large state spaces inevitably needs to balance depth and breadth of the search.
Uncertainty - sensitive learning and planning with ensembles
Notably, our method performs well in environments with sparse rewards where standard $TD(1)$ backups fail.
Developmentally motivated emergence of compositional communication via template transfer
This paper explores a novel approach to achieving emergent compositional communication in multi-agent systems.
Trust, but verify: model-based exploration in sparse reward environments
We propose $\textit{trust-but-verify}$ (TBV) mechanism, a new method which uses model uncertainty estimates to guide exploration.
Model-based Reinforcement Learning
reinforcement-learning
+1
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Communication is compositional if complex signals can be represented as a combination of simpler subparts.
Emergence of compositional language in communication through noisy channel
In this paper, we investigate how communication through a noisy channel can lead to the emergence of compositional language.
Disentangling Transfer in Continual Reinforcement Learning
The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, limiting the applicability of continual learning solutions to realistic scenarios.
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.
Magnushammer: A Transformer-Based Approach to Premise Selection
By combining \method with a language-model-based automated theorem prover, we further improve the state-of-the-art proof success rate from $57. 0\%$ to $71. 0\%$ on the PISA benchmark using $4$x fewer parameters.
Structured Packing in LLM Training Improves Long Context Utilization
Recent developments in long-context large language models have attracted considerable attention.
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models.
tsGT: Stochastic Time Series Modeling With Transformer
Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data.
