arXiv research

DeepAveragers solves offline RL by solving derived MDPs from static data.

We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intractable to solve with typical reinforcement learning (RL) algorithms especially when the number of items is huge. In this paper, we present …

Unified framework for solving MDPs with stochastic mirror descent.

We consider large-scale Markov decision processes (MDPs) with parameter uncertainty, under the robust MDP paradigm. Previous studies showed that robust MDPs, based on a minimax approach to handle uncertainty, can be solved using dynamic programming for small to medium sized problems. However, due to the "curse of dimen…

We solve POMDPs by approximating them as finite-state MDPs.

New method removes oracle and reduces memory usage for robust MDPs.

New algorithms solve robust MDPs efficiently, significantly faster than existing methods.

Paper identifies reductive MDPs, solving them in polynomial time.

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision making under uncertainty. The classical approaches for solving MDPs are well known and have been widely studied, some of which rely on approximation techniques to solve MDPs with large state space and/or action space. However…

Adaptive reduction scheme approximates optimal policy in regularized MDPs.

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints. In this study, we propose a novel simulator-based method to approximately solve a CMDP pr…

Faster algorithms for solving multichain MDPs under average-reward criterion.

In this paper we derive an efficient method for computing the indices associated with an asymptotically optimal upper confidence bound algorithm (MDP-UCB) of Burnetas and Katehakis (1997) that only requires solving a system of two non-linear equations with two unknowns, irrespective of the cardinality of the state spac…

New method approximates POMDPs with PB-MDPs, providing error bounds and practical algorithms.

New RL approach tackles non-linear MDPs without linear assumptions.

Optimistic PPO variant solves linear MDPs with improved regret bound.

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…

A new method for solving complex sequential decision-making problems by decomposing them into multiple levels.

Deep nets solve MDPs without high dimensions.

Reward suffices for convex MDPs, expanding RL to new problems.

The paper introduces MDP homomorphic networks for faster reinforcement learning.

Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy. In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces. It is b…

Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decision processes using risk measures, it has not addressed the problem of solving large risk-averse formulations. In this paper, we propose and analyze a new method for solving large risk-averse …

New bounds for learning near-optimal policies in CMDPs with constraints.

Proposes a Riemannian optimization for policy improvement in MDPs.

Paper bounds PAC RL sample complexity in deterministic MDPs.

We introduce SCAL, an algorithm designed to perform efficient exploration-exploitation in any unknown weakly-communicating Markov decision process (MDP) for which an upper bound $c$ on the span of the optimal bias function is known. For an MDP with $S$ states, $A$ actions and $Γ\leq S$ possible next states, we prove a …

Study on policy testing in MDPs with lower bounds and new algorithm.

Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the …

The paper tackles reinforcement learning with exogenous variables and rewards.

RL agents learn from a few tasks to generalize to new ones.

Reward-free RL in linear MDPs is as hard as reward-aware RL.

In this paper, we settle the sampling complexity of solving discounted two-player turn-based zero-sum stochastic games up to polylogarithmic factors. Given a stochastic game with discount factor $γ\in(0,1)$ we provide an algorithm that computes an $ε$-optimal strategy with high-probability given $\tilde{O}((1 - γ)^{-3}…

New algorithm reduces switching costs in RL beyond linear MDPs.

We use deep reinforcement learning to optimize experimental designs efficiently.

New state-only IL algorithm tackles MDP transition mismatch.

This paper presents a way of solving Markov Decision Processes that combines state abstraction and temporal abstraction. Specifically, we combine state aggregation with the options framework and demonstrate that they work well together and indeed it is only after one combines the two that the full benefit of each is re…

In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient (SVRG) methods have proven to be very successful in supervised learning. However, their adaptation…

Study minimizes risk in MDPs with spectral measures.

New method finds exact Pareto front for MO-MDPs efficiently.

Paper analyzes convergence of dynamic policy gradient for MDPs, improving performance in finite-time problems.

Approximate linear programming (ALP) represents one of the major algorithmic families to solve large-scale Markov decision processes (MDP). In this work, we study a primal-dual formulation of the ALP, and develop a scalable, model-free algorithm called bilinear $π$ learning for reinforcement learning when a sampling or…

Batch Reinforcement Learning (Batch RL) consists in training a policy using trajectories collected with another policy, called the behavioural policy. Safe policy improvement (SPI) provides guarantees with high probability that the trained policy performs better than the behavioural policy, also called baseline in this…

We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to estimate the mean value of each state as accurately as possible. Similarly to active exploration in multi-armed bandit (MAB), states may have…

New algorithm learns sparse linear MDPs with polynomial interactions, improving sample complexity.

New approach connects reinforcement learning robustness and regularization.

Paper shows RLHF can be solved similarly to standard RL.

Develops a regression approach for solving MDPs with general state and action spaces.