Janarthanan Rajendran

Aditya Mahajan

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Hadi Nekoei

Miao Liu

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

Arjun Vaithilingam Sudhakar

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

Prasanna Parthasarathi

2023-11-13

ArXiv (preprint)

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Hadi Nekoei

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.

2023-08-20

ArXiv (preprint)

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

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.

2023-05-08

auai.org/UAI/2023/Conference (published)

Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning

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 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.

2023-05-08

auai.org/UAI/2023/Conference (published)

Behavioral Cloning for Crystal Design

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

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.

2023-03-17

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

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amin Amini

Aditya Mahajan

2023-02-06

ArXiv (preprint)

Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning

Ali Rahimi-Kalahroudi

Ida Momennejad

Harm van Seijen

2023-01-01

CoLLAs (published)

proceedings.mlr.press

Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning

Ali Rahimi-Kalahroudi

Ida Momennejad

Harm van Seijen

One of the key behavioral characteristics used in neuroscience to determine whether the subject of study—be it a rodent or a human—exhib… (see more)its model-based learning is effective adaptation to local changes in the environment. In reinforcement learning, however, recent work has shown that modern deep model-based reinforcement-learning (MBRL) methods adapt poorly to such changes. An explanation for this mismatch is that MBRL methods are typically designed with sample-efﬁciency on a single task in mind and the requirements for effective adaptation are substantially higher, both in terms of the learned world model and the planning routine. One particularly challenging requirement is that the learned world model has to be sufﬁciently accurate throughout relevant parts of the state-space. This is challenging for deep-learning-based world models due to catastrophic forgetting. And while a replay buffer can mitigate the effects of catastrophic forgetting, the traditional ﬁrst-in-ﬁrst-out replay buffer precludes effective adaptation due to maintaining stale data. In this work

2022-12-09

NeurIPS.cc/2022/Workshop/DeepRL (unknown)

PatchBlender: A Motion Prior for Video Transformers

Gabriele Prato

Yale Song

(Rex) Devon Hjelm

Neel Joshi

2022-11-11

ArXiv (preprint)

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

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amini

Aditya Mahajan

We empirically show that classic ideas from two-time scale stochastic approximation \citep{borkar1997stochastic} can be combined with sequen… (see more)tial iterative best response (SIBR) to solve complex cooperative multi-agent reinforcement learning (MARL) problems. We first start with giving a multi-agent estimation problem as a motivating example where SIBR converges while parallel iterative best response (PIBR) does not. Then we present a general implementation of staged multi-agent RL algorithms based on SIBR and multi-time scale stochastic approximation, and show that our new methods which we call Staged Independent Proximal Policy Optimization (SIPPO) and Staged Independent Q-learning (SIQL) outperform state-of-the-art independent learning on almost all the tasks in the epymarl \citep{papoudakis2020benchmarking} benchmark. This can be seen as a first step towards more decentralized MARL methods based on SIBR and multi-time scale learning.

2022-04-25

ICLR.cc/2022/Workshop/GMS (published)