Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

Reward-free exploration is a reinforcement learning setting recently studied by (Jin et al.
2020), who address it by running several algorithms with regret guarantees in parallel. In our …

Exploration in environments which differ across episodes has received increasing attention
in recent years. Current methods use some combination of global novelty bonuses …

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 …

Exploration is essential for solving complex Reinforcement Learning (RL) tasks. Maximum
State-Visitation Entropy (MSVE) formulates the exploration problem as a well-defined policy …

Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an
agent has to reach a goal state in minimum total expected cost. In the learning formulation of …

We study the problem of learning in the stochastic shortest path (SSP) setting, where an
agent seeks to minimize the expected cost accumulated before reaching a goal state. We …

We study the stochastic shortest path problem with adversarial costs and known transition,
and show that the minimax regret is $ O (\sqrt {DT_\star K}) $ and $ O (\sqrt {DT_\star SA K}) …

We make significant progress toward the stochastic shortest path problem with adversarial
costs and unknown transition. Specifically, we develop algorithms that achieve $ O (\sqrt {S …

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 …