Search Results for author:
Found 19 papers, 11 papers with code
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
In this work, we conduct new analysis demonstrating that SMAC lacks the stochasticity and partial observability to require complex *closed-loop* policies.
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria
By directly comparing active equilibria to Nash equilibria in these examples, we find that active equilibria find more effective solutions than Nash equilibria, concluding that an active equilibrium is the desired solution for multiagent learning settings.
Learning to Optimize Quasi-Newton Methods
This paper introduces a novel machine learning optimizer called LODO, which tries to online meta-learn the best preconditioner during optimization.
Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
Autonomous agents deployed in the real world need to be robust against adversarial attacks on sensory inputs.
K-level Reasoning for Zero-Shot Coordination in Hanabi
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together.
Self-Explaining Deviations for Coordination
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world.
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
An effective approach that has recently emerged for addressing this non-stationarity is for each agent to anticipate the learning of other agents and influence the evolution of future policies towards desirable behavior for its own benefit.
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization
Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization.
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Robust Visual Domain Randomization for Reinforcement Learning
Producing agents that can generalize to a wide range of visually different environments is a significant challenge in reinforcement learning.
Robust Domain Randomization for Reinforcement Learning
In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.
Exploratory Combinatorial Optimization with Reinforcement Learning
Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph.
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making.
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Multi-Agent Common Knowledge Reinforcement Learning
In this paper, we show that common knowledge between agents allows for complex decentralised coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Learning with Opponent-Learning Awareness
We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL.
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
There exist many problem domains where the interpretability of neural network models is essential for deployment.
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks.
