Search Results for author:
Found 17 papers, 14 papers with code
Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding
Our approach utilizes the agent's observations to recreate the intrinsic Markov decision process, which is then used for planning with a tailored for multi-agent tasks version of neural MCTS.
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.
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion.
Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results
We investigate how to utilize Monte-Carlo Tree Search (MCTS) to solve the problem.
Reinforcement Learning with Success Induced Task Prioritization
Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies.
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
Human intelligence can remarkably adapt quickly to new tasks and environments.
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy.
Pathfinding in stochastic environments: learning vs planning
Within planning, an agent constantly re-plans and updates the path based on the history of the observations using a search-based planner.
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.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Long-Term Exploration in Persistent MDPs
Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy.
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
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Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft
We present Hierarchical Deep Q-Network (HDQfD) that took first place in the MineRL competition.
