Search Results for author:
Found 17 papers, 5 papers with code
A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry.
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner.
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem.
Model-Based Decentralized Policy Optimization
To help the policy improvement be stable and monotonic, we propose model-based decentralized policy optimization (MDPO), which incorporates a latent variable function to help construct the transition and reward function from an individual perspective.
Best Possible Q-Learning
To tackle this challenge, we propose best possible operator, a novel decentralized operator, and prove that the policies of agents will converge to the optimal joint policy if each agent independently updates its individual state-action value by the operator.
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL).
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning
OTC is simple yet effective to increase data efficiency and improve agent policies in online tuning.
Multi-agent Reinforcement Learning
reinforcement-learning
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Offline Decentralized Multi-Agent Reinforcement Learning
In this paper, we propose a framework for offline decentralized multi-agent reinforcement learning, which exploits value deviation and transition normalization to deliberately modify the transition probabilities.
Model-Based Opponent Modeling
When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before.
Adaptive Learning Rates for Multi-Agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), the learning rates of actors and critic are mostly hand-tuned and fixed.
Multi-agent Reinforcement Learning
reinforcement-learning
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The Emergence of Individuality in Multi-Agent Reinforcement Learning
EOI learns a probabilistic classifier that predicts a probability distribution over agents given their observation and gives each agent an intrinsic reward of being correctly predicted by the classifier.
Multi-agent Reinforcement Learning
reinforcement-learning
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The Emergence of Individuality
EOI learns a probabilistic classifier that predicts a probability distribution over agents given their observation and gives each agent an intrinsic reward of being correctly predicted by the classifier.
Learning Fairness in Multi-Agent Systems
Fairness is essential for human society, contributing to stability and productivity.
Generative Exploration and Exploitation
Sparse reward is one of the biggest challenges in reinforcement learning (RL).
Graph Convolutional Reinforcement Learning
The key is to understand the mutual interplay between agents.
Learning Attentional Communication for Multi-Agent Cooperation
Our model leads to efficient and effective communication for large-scale multi-agent cooperation.
