We develop provably efficient reinforcement learning algorithms for twoplayer zerosum finitehorizon Markov games with simultaneous moves. 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 aim to find the Nash equilibrium by minimizing the duality gap. In the online setting, we control a single player playing against an arbitrary opponent and aim to minimize the regret. For both settings, we propose an optimistic variant of the leastsquares minimax value iteration algorithm. We show that our algorithm is computationally efficient and provably achieves an [Formula: see text] upper bound on the duality gap and regret, where d is the linear dimension, H the horizon and T the total number of timesteps. Our results do not require additional assumptions on the sampling model. Our setting requires overcoming several new challenges that are absent in Markov decision processes or turnbased Markov games. In particular, to achieve optimism with simultaneous moves, we construct both upper and lower confidence bounds of the value function, and then compute the optimistic policy by solvingmore »
XDO: A Double Oracle Algorithm for ExtensiveForm Games
Policy Space Response Oracles (PSRO) is a reinforcement learning (RL) algo
rithm for twoplayer zerosum games that has been empirically shown to find
approximate Nash equilibria in large games. Although PSRO is guaranteed to
converge to an approximate Nash equilibrium and can handle continuous actions,
it may take an exponential number of iterations as the number of information
states (infostates) grows. We propose ExtensiveForm Double Oracle (XDO), an
extensiveform double oracle algorithm for twoplayer zerosum games that is guar
anteed to converge to an approximate Nash equilibrium linearly in the number of
infostates. Unlike PSRO, which mixes best responses at the root of the game, XDO
mixes best responses at every infostate. We also introduce Neural XDO (NXDO),
where the best response is learned through deep RL. In tabular experiments on
Leduc poker, we find that XDO achieves an approximate Nash equilibrium in a
number of iterations an order of magnitude smaller than PSRO. Experiments on
a modified Leduc poker game and OshiZumo show that tabular XDO achieves
a lower exploitability than CFR with the same amount of computation. We also
find that NXDO outperforms PSRO and NFSP on a sequential multidimensional
continuousaction game. NXDO is the first deep RL method that can find an
approximate Nash equilibrium in highdimensional continuousaction sequential
games. Experiment code is available at more »
 Award ID(s):
 1839429
 Publication Date:
 NSFPAR ID:
 10313270
 Journal Name:
 Advances in neural information processing systems
 ISSN:
 10495258
 Sponsoring Org:
 National Science Foundation
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