Search Results for author:
Found 19 papers, 9 papers with code
Object-Centric Learning with Slot Mixture Module
Object-centric architectures usually apply a differentiable module to the entire feature map to decompose it into sets of entity representations called slots.
Gradual Optimization Learning for Conformational Energy Minimization
Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules using $50$x less additional data.
Graphical Object-Centric Actor-Critic
Our algorithm performs better in a visually complex 3D robotic environment and a 2D environment with compositional structure than the state-of-the-art model-free actor-critic algorithm built upon transformer architecture and the state-of-the-art monolithic model-based algorithm.
Learning Successor Features with Distributed Hebbian Temporal Memory
This paper presents a novel approach to address the challenge of online temporal memory learning for decision-making under uncertainty in non-stationary, partially observable environments.
Recurrent Action Transformer with Memory
One solution to this problem is to enhance transformers with memory mechanisms.
Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review
The reinforcement learning research area contains a wide range of methods for solving the problems of intelligent agent control.
Model-based Reinforcement Learning
reinforcement-learning
+1
HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images
To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR.
POGEMA: Partially Observable Grid Environment for Multiple Agents
We introduce POGEMA (https://github. com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems .
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way.
Multitask Adaptation by Retrospective Exploration with Learned World Models
Model-based reinforcement learning (MBRL) allows solving complex tasks in a sample-efficient manner.
Long-Term Exploration in Persistent MDPs
Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy.
Landmark Policy Optimization for Object Navigation Task
This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments.
Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments
In this paper, we consider the problem of multi-agent navigation in partially observable grid environments.
Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations
There are two main approaches to improving the sample efficiency of reinforcement learning methods - using hierarchical methods and expert demonstrations.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Delta Schema Network in Model-based Reinforcement Learning
This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft
We present Hierarchical Deep Q-Network (HDQfD) that took first place in the MineRL competition.
Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering
We introduce a new approach to hierarchy formation and task decomposition in hierarchical reinforcement learning.
Behavior and path planning for the coalition of cognitive robots in smart relocation tasks
In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location.
Psychologically inspired planning method for smart relocation task
On the subsymbolic level the task of path planning is considered and solved as a graph search problem.
