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Found 10 papers, 0 papers with code
Assume-Guarantee Reinforcement Learning
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel.
Omega-Regular Decision Processes
Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback).
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs
Linear temporal logic (LTL) and omega-regular objectives -- a superset of LTL -- have seen recent use as a way to express non-Markovian objectives in reinforcement learning.
Omega-Regular Reward Machines
Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success.
Policy Synthesis and Reinforcement Learning for Discounted LTL
The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL).
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems
A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems.
Recursive Reinforcement Learning
Recursion is the fundamental paradigm to finitely describe potentially infinite objects.
Alternating Good-for-MDP Automata
The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDP property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDP automata we produce from deterministic Streett automata are bi-linear in the the size of the deterministic automaton and its index, and can therefore be exponentially more succinct than minimal nondeterministic B\"uchi automata.
Mungojerrie: Reinforcement Learning of Linear-Time Objectives
Reinforcement learning synthesizes controllers without prior knowledge of the system.
Model-free Reinforcement Learning for Branching Markov Decision Processes
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs).
