Collaborating Authors

On Reward-Free Reinforcement Learning with Linear Function Approximation Artificial Intelligence

Reward-free reinforcement learning (RL) is a framework which is suitable for both the batch RL setting and the setting where there are many reward functions of interest. During the exploration phase, an agent collects samples without using a pre-specified reward function. After the exploration phase, a reward function is given, and the agent uses samples collected during the exploration phase to compute a near-optimal policy. Jin et al. [2020] showed that in the tabular setting, the agent only needs to collect polynomial number of samples (in terms of the number states, the number of actions, and the planning horizon) for reward-free RL. However, in practice, the number of states and actions can be large, and thus function approximation schemes are required for generalization. In this work, we give both positive and negative results for reward-free RL with linear function approximation. We give an algorithm for reward-free RL in the linear Markov decision process setting where both the transition and the reward admit linear representations. The sample complexity of our algorithm is polynomial in the feature dimension and the planning horizon, and is completely independent of the number of states and actions. We further give an exponential lower bound for reward-free RL in the setting where only the optimal $Q$-function admits a linear representation. Our results imply several interesting exponential separations on the sample complexity of reward-free RL.

A Simple Reward-free Approach to Constrained Reinforcement Learning Artificial Intelligence

In a wide range of modern reinforcement learning (RL) applications, it is not sufficient for the learning agents to only maximize a scalar reward. More importantly, they must satisfy various constraints. For instance, such constraints can be the physical limit of power consumption or torque in motors for robotics tasks [27]; the budget for computation and the frequency of actions for real-time strategy games [28]; and the requirement for safety, fuel efficiency and human comfort for autonomous drive [16]. In addition, constraints are also crucial in tasks such as dynamic pricing with limited supply [5, 4], scheduling of resources on a computer cluster [18], imitation learning [26, 35, 25], as well as reinforcement learning with fairness [12]. These huge demand in practice gives rise to a subfield--constrained RL, which focuses on designing efficient algorithms to find near-optimal policies for RL problems under linear or general convex constraints. Most constrained RL works directly combine the existing techniques such as value iteration and optimism from unconstrained literature, with new techniques specifically designed to deal with linear constraints [9, 8, 22] or general convex constraints [7, 32]. The end product is a single new complex algorithm which is tasked to solve all the challenges of learning dynamics, exploration, planning as well as constraints satisfaction simultaneously.

Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium Machine Learning

We develop provably efficient reinforcement learning algorithms for two-player zero-sum Markov games in which the two players simultaneously take actions. To incorporate function approximation, we consider a family of Markov games where the reward function and transition kernel possess a linear structure. Both the offline and online settings of the problems are considered. In the offline setting, we control both players and the goal is to find the Nash Equilibrium efficiently by minimizing the worst-case duality gap. In the online setting, we control a single player to play against an arbitrary opponent and the goal is to minimize the regret. For both settings, we propose an optimistic variant of the least-squares minimax value iteration algorithm. We show that our algorithm is computationally efficient and provably achieves an $\tilde O(\sqrt{d^3 H^3 T})$ upper bound on the duality gap and regret, without requiring additional assumptions on the sampling model. We highlight that our setting requires overcoming several new challenges that are absent in Markov decision processes or turn-based Markov games. In particular, to achieve optimism in simultaneous-move Marko games, we construct both upper and lower confidence bounds of the value function, and then compute the optimistic policy by solving a general-sum matrix game with these bounds as the payoff matrices. As finding the Nash Equilibrium of such a general-sum game is computationally hard, our algorithm instead solves for a Coarse Correlated Equilibrium (CCE), which can be obtained efficiently via linear programming. To our best knowledge, such a CCE-based scheme for implementing optimism has not appeared in the literature and might be of interest in its own right.

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? Machine Learning

Reinforcement learning (RL) has achieved striking empirical successes in solving real-world sequential decision-making problems (Mnih et al., 2015; Duan et al., 2016; Silver et al., 2016, 2017, 2018; Agostinelli et al., 2019; Akkaya et al., 2019). Motivated by these successes, multi-agent extensions of RL algorithms have recently become popular in decision-making problems involving multiple interacting agents (Busoniu et al., 2008; Hernandez-Leal et al., 2018, 2019; OroojlooyJadid and Hajinezhad, 2019; Zhang et al., 2019). Multi-agent RL is often modeled as a Markov game (Littman, 1994) where, at each time step, given the state of the environment, each player (agent) takes an action simultaneously, observes her own immediate reward, and the environment evolves into a next state. Here both the reward of each player and the state transition depend on the actions of all players. From the perspective of each player, her goal is to find a policy that maximizes her expected total reward in the presence of other agents. In Markov games, depending on the structure of the reward functions, the relationship among the players can be either collaborative, where each player has the same reward function, or competitive, where the sum of the reward function is equal to zero, or mixed, which corresponds to a general-sum game. While most of the existing theoretical results focus on the collaborative or two-player competitive settings, the mixed setting is oftentimes more pertinent to real-world multi-agent applications. Moreover, in addition to having diverse reward functions, the players might also have asymmetric roles in the Markov game--the players might be divided into leaders and followers, where the leaders' joint policy determines a general-sum game for the followers.

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play Artificial Intelligence

Model-based algorithms---algorithms that decouple learning of the model and planning given the model---are widely used in reinforcement learning practice and theoretically shown to achieve optimal sample efficiency for single-agent reinforcement learning in Markov Decision Processes (MDPs). However, for multi-agent reinforcement learning in Markov games, the current best known sample complexity for model-based algorithms is rather suboptimal and compares unfavorably against recent model-free approaches. In this paper, we present a sharp analysis of model-based self-play algorithms for multi-agent Markov games. We design an algorithm \emph{Optimistic Nash Value Iteration} (Nash-VI) for two-player zero-sum Markov games that is able to output an $\epsilon$-approximate Nash policy in $\tilde{\mathcal{O}}(H^3SAB/\epsilon^2)$ episodes of game playing, where $S$ is the number of states, $A,B$ are the number of actions for the two players respectively, and $H$ is the horizon length. This is the first algorithm that matches the information-theoretic lower bound $\Omega(H^3S(A+B)/\epsilon^2)$ except for a $\min\{A,B\}$ factor, and compares favorably against the best known model-free algorithm if $\min\{A,B\}=o(H^3)$. In addition, our Nash-VI outputs a single Markov policy with optimality guarantee, while existing sample-efficient model-free algorithms output a nested mixture of Markov policies that is in general non-Markov and rather inconvenient to store and execute. We further adapt our analysis to designing a provably efficient task-agnostic algorithm for zero-sum Markov games, and designing the first line of provably sample-efficient algorithms for multi-player general-sum Markov games.