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Found 12 papers, 3 papers with code
Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains.
Decision Transformer as a Foundation Model for Partially Observable Continuous Control
Closed-loop control of nonlinear dynamical systems with partial-state observability demands expert knowledge of a diverse, less standardized set of theoretical tools.
$\widetilde{O}(T^{-1})$ Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games
No-regret learning has a long history of being closely connected to game theory.
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
This project serves the learning for dynamics & control (L4DC) community, aiming to explore key questions: the convergence of RL algorithms in learning control policies; the stability and robustness issues of learning-based controllers; and the scalability of RL algorithms to high- and potentially infinite-dimensional systems.
Action Dynamics Task Graphs for Learning Plannable Representations of Procedural Tasks
Given video demonstrations and paired narrations of an at-home procedural task such as changing a tire, we present an approach to extract the underlying task structure -- relevant actions and their temporal dependencies -- via action-centric task graphs.
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential sample complexity dependence on the number of agents, a phenomenon known as \emph{the curse of multiagents}.
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games
We show that the agents can find an $\epsilon$-approximate CCE in at most $\widetilde{O}( H^6S A /\epsilon^2)$ episodes, where $S$ is the number of states, $A$ is the size of the largest individual action space, and $H$ is the length of an episode.
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration
Many real-world applications of multi-agent reinforcement learning (RL), such as multi-robot navigation and decentralized control of cyber-physical systems, involve the cooperation of agents as a team with aligned objectives.
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes.
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes (MDPs).
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis
Monte-Carlo planning, as exemplified by Monte-Carlo Tree Search (MCTS), has demonstrated remarkable performance in applications with finite spaces.
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
To enable the development of tractable algorithms, we introduce the concept of an information state embedding that serves to compress agents' histories.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
