VOL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
We study time-inhomogeneous episodic reinforcement learning (RL) under general function
approximation and sparse rewards. We design a new algorithm, Variance-weighted …
approximation and sparse rewards. We design a new algorithm, Variance-weighted …
Computationally efficient horizon-free reinforcement learning for linear mixture mdps
Recent studies have shown that episodic reinforcement learning (RL) is not more difficult
than bandits, even with a long planning horizon and unknown state transitions. However …
than bandits, even with a long planning horizon and unknown state transitions. However …
Settling the sample complexity of online reinforcement learning
A central issue lying at the heart of online reinforcement learning (RL) is data efficiency.
While a number of recent works achieved asymptotically minimal regret in online RL, the …
While a number of recent works achieved asymptotically minimal regret in online RL, the …
Horizon-free reinforcement learning in polynomial time: the power of stationary policies
This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes
(MDP) that enjoys a regret bound\emph {independent on the planning horizon}. Specifically …
(MDP) that enjoys a regret bound\emph {independent on the planning horizon}. Specifically …
Learning stochastic shortest path with linear function approximation
We study the stochastic shortest path (SSP) problem in reinforcement learning with linear
function approximation, where the transition kernel is represented as a linear mixture of …
function approximation, where the transition kernel is represented as a linear mixture of …
Sharp variance-dependent bounds in reinforcement learning: Best of both worlds in stochastic and deterministic environments
We study variance-dependent regret bounds for Markov decision processes (MDPs).
Algorithms with variance-dependent regret guarantees can automatically exploit …
Algorithms with variance-dependent regret guarantees can automatically exploit …
Optimal horizon-free reward-free exploration for linear mixture mdps
We study reward-free reinforcement learning (RL) with linear function approximation, where
the agent works in two phases:(1) in the exploration phase, the agent interacts with the …
the agent works in two phases:(1) in the exploration phase, the agent interacts with the …
Improved no-regret algorithms for stochastic shortest path with linear mdp
We introduce two new no-regret algorithms for the stochastic shortest path (SSP) problem
with a linear MDP that significantly improve over the only existing results of (Vial et al …
with a linear MDP that significantly improve over the only existing results of (Vial et al …
Minimax regret for stochastic shortest path
Abstract We study the Stochastic Shortest Path (SSP) problem in which an agent has to
reach a goal state in minimum total expected cost. In the learning formulation of the problem …
reach a goal state in minimum total expected cost. In the learning formulation of the problem …
Implicit finite-horizon approximation and efficient optimal algorithms for stochastic shortest path
L Chen, M Jafarnia-Jahromi… - Advances in Neural …, 2021 - proceedings.neurips.cc
We introduce a generic template for developing regret minimization algorithms in the
Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as …
Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as …