Search Results for author:
Found 17 papers, 11 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.
Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
To that purpose, we offer a new leader-follower bilevel framework that is capable of learning to ask relevant questions (prompts) and subsequently undertaking reasoning to guide the learning of actions.
Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons.
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II.
Revisiting the Gumbel-Softmax in MADDPG
This method, however, is statistically biased, and a recent MARL benchmarking paper suggests that this bias makes MADDPG perform poorly in grid-world situations, where the action space is discrete.
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles.
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.
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
We show that a team of agents is able to adapt to novel tasks when provided with task embeddings.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong dependency on hyperparameters.
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.
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training.
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.
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards.
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.
