Search Results for author:
Found 11 papers, 5 papers with code
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse.
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
This work focuses on equilibrium selection in no-conflict multi-agent games, where we specifically study the problem of selecting a Pareto-optimal Nash equilibrium among several existing equilibria.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Local Information Opponent Modelling Using Variational Autoencoders
Modelling the behaviours of other agents (opponents) is essential for understanding how agents interact and making effective decisions.
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult.
Variational Autoencoders for Opponent Modeling in Multi-Agent Systems
Modeling the behavior of other agents (opponents) is essential in understanding the interactions of the agents in the system.
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains.
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks
In this paper we propose a divide and conquer deep reinforcement learning solution and we test our agent in the first person shooter (FPS) game of Doom.
