Janarthanan Rajendran

Miao Liu

Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in rece… (see more)nt years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners. To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods. In particular, we created a diverse set of pre-trained agents and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance. After evaluating several SOTA algorithms using our framework, our experiments reveal that naive Independent Q-Learning (IQL) agents in most cases adapt as quickly as the SOTA ZSC algorithm Off-Belief Learning (OBL). This finding raises an interesting research question: How to design MARL algorithms with high ZSC performance and capability of fast adaptation to unseen partners. As a first step, we studied the role of different hyper-parameters and design choices on the adaptability of current MARL algorithms. Our experiments show that two categories of hyper-parameters controlling the training data diversity and optimization process have a significant impact on the adaptability of Hanabi agents.

2023-11-19

Conference on Lifelong Learning Agents (published)

Arjun Vaithilingam Sudhakar

proceedings.mlr.press

Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games

Prasanna Parthasarathi

2023-11-12

arXiv (preprint)

arxiv.org

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

Xutong Zhao

Yangchen Pan

Chenjun Xiao

Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration met… (see more)hod that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at \textit{each environment timestep}, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent's state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.

2023-07-01

Conference on Uncertainty in Artificial Intelligence (published)

proceedings.mlr.press

Behavioral Cloning for Crystal Design

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

A. Chandar

Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications… (see more) such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.

2023-03-16

ICLR.cc/2023/Workshop/ML4Materials (poster)

openreview.net

PatchBlender: A Motion Prior for Video Transformers

Gabriele Prato

Yale Song

R Devon Hjelm

Neel Joshi

A. Chandar

2022-11-10

ArXiv (preprint)

openreview.net

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

Yi Wan

Ali Rahimi-Kalahroudi

Ida Momennejad

Harm van Seijen

In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-b… (see more)ased RL is not surprising, given its many potential benefits, such as higher sample efficiency and the potential for fast adaption to changes in the environment. However, we demonstrate, using an improved version of the recently introduced Local Change Adaptation (LoCA) setup, that well-known model-based methods such as PlaNet and DreamerV2 perform poorly in their ability to adapt to local environmental changes. Combined with prior work that made a similar observation about the other popular model-based method, MuZero, a trend appears to emerge, suggesting that current deep model-based methods have serious limitations. We dive deeper into the causes of this poor performance, by identifying elements that hurt adaptive behavior and linking these to underlying techniques frequently used in deep model-based RL. We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes. Furthermore, we provide detailed insights into the challenges of building an adaptive nonlinear model-based method, by experimenting with a nonlinear version of Dyna.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

proceedings.mlr.press

Staged independent learning: Towards decentralized cooperative multi-agent Reinforcement Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amini