Search Results for author:
Found 22 papers, 7 papers with code
“Other-Play” for Zero-Shot Coordination
We consider the problem of zero-shot coordination - constructing AI agents that can coordinate with novel partners they have not seen before (e. g. humans).
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Imitation Bootstrapped Reinforcement Learning
Despite the considerable potential of reinforcement learning (RL), robotic control tasks predominantly rely on imitation learning (IL) due to its better sample efficiency.
Toward Grounded Commonsense Reasoning
We additionally illustrate our approach with a robot on 2 carefully designed surfaces.
The Update-Equivalence Framework for Decision-Time Planning
Using this framework, we derive a provably sound search algorithm for fully cooperative games based on mirror descent and a search algorithm for adversarial games based on magnetic mirror descent.
Language Instructed Reinforcement Learning for Human-AI Coordination
One of the fundamental quests of AI is to produce agents that coordinate well with humans.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Human-AI Coordination via Human-Regularized Search and Learning
First, we show that our method outperforms experts when playing with a group of diverse human players in ad-hoc teams.
K-level Reasoning for Zero-Shot Coordination in Hanabi
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together.
Self-Explaining Deviations for Coordination
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world.
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior.
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker.
A Fine-Tuning Approach to Belief State Modeling
Furthermore, because this specialization occurs after the action or policy has already been decided, BFT does not require the belief model to process it as input.
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark fully and partially observable games.
Off-Belief Learning
Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
In the era of ever decreasing loss functions, SGD and its various offspring have become the go-to optimization tool in machine learning and are a key component of the success of deep neural networks (DNNs).
"Other-Play" for Zero-Shot Coordination
We consider the problem of zero-shot coordination - constructing AI agents that can coordinate with novel partners they have not seen before (e. g. humans).
Polygames: Improved Zero Learning
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
Improving Policies via Search in Cooperative Partially Observable Games
The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy.
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Hierarchical Decision Making by Generating and Following Natural Language Instructions
We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making.
Learning Deep Generative Models With Discrete Latent Variables
There have been numerous recent advancements on learning deep generative models with latent variables thanks to the reparameterization trick that allows to train deep directed models effectively.
Deep Restricted Boltzmann Networks
The most famous ones among them are deep belief network, which stacks multiple layer-wise pretrained RBMs to form a hybrid model, and deep Boltzmann machine, which allows connections between hidden units to form a multi-layer structure.
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
We alternate the pruning and retraining to further reduce zero activations in a network.
