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Found 11 papers, 3 papers with code
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
Artificially intelligent agents deployed in the real-world will require the ability to reliably \textit{cooperate} with humans (as well as other, heterogeneous AI agents).
AI4GCC - Team: Below Sea Level: Critiques and Improvements
We present a critical analysis of the simulation framework RICE-N, an integrated assessment model (IAM) for evaluating the impacts of climate change on the economy.
AI4GCC-Team -- Below Sea Level: Score and Real World Relevance
As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation.
Towards a Unifying Model of Rationality in Multiagent Systems
Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another.
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
A natural solution concept in such settings is the Stackelberg equilibrium, in which the ``leader'' agent selects the strategy that maximizes its own payoff given that the ``follower'' agent will choose their best response to this strategy.
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
Learning to cooperate with other agents is challenging when those agents also possess the ability to adapt to our own behavior.
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning
High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems.
Better Exploration with Optimistic Actor Critic
To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function.
Better Exploration with Optimistic Actor-Critic
To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function.
Interactive Learning of Environment Dynamics for Sequential Tasks
In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but also the structure and dynamics of that user's environment.
Interactive Learning from Policy-Dependent Human Feedback
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback.
