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Found 16 papers, 7 papers with code
From Motor Control to Team Play in Simulated Humanoid Football
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals.
Discovering Reinforcement Learning Algorithms
Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments.
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning
We employ a temporal hierarchy, using a slow-ticking recurrent core to allow information to flow more easily over long time spans, and three fast-ticking recurrent cores with connections designed to create an information asymmetry.
Navigating the Landscape of Multiplayer Games
Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence.
Multiplicative Interactions and Where to Find Them
We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.
Smooth markets: A basic mechanism for organizing gradient-based learners
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.
Information asymmetry in KL-regularized RL
In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning.
α-Rank: Multi-Agent Evaluation by Evolution
We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.
Adapting Auxiliary Losses Using Gradient Similarity
One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations.
Evolving intrinsic motivations for altruistic behavior
Multi-agent cooperation is an important feature of the natural world.
Multiagent Systems
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games.
Progress & Compress: A scalable framework for continual learning
This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task.
Kickstarting Deep Reinforcement Learning
Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance.
Population Based Training of Neural Networks
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm.
