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Found 5 papers, 3 papers with code
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
Safe Policy Learning for Continuous Control
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i. e.,~policies that keep the agent in desirable situations, both during training and at convergence.
Neural Replicator Dynamics
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning.
Lyapunov-based Safe Policy Optimization for Continuous Control
We formulate these problems as constrained Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.
A Lyapunov-based Approach to Safe Reinforcement Learning
In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints.
