6,853 research outputs found
A new Potential-Based Reward Shaping for Reinforcement Learning Agent
Potential-based reward shaping (PBRS) is a particular category of machine
learning methods which aims to improve the learning speed of a reinforcement
learning agent by extracting and utilizing extra knowledge while performing a
task. There are two steps in the process of transfer learning: extracting
knowledge from previously learned tasks and transferring that knowledge to use
it in a target task. The latter step is well discussed in the literature with
various methods being proposed for it, while the former has been explored less.
With this in mind, the type of knowledge that is transmitted is very important
and can lead to considerable improvement. Among the literature of both the
transfer learning and the potential-based reward shaping, a subject that has
never been addressed is the knowledge gathered during the learning process
itself. In this paper, we presented a novel potential-based reward shaping
method that attempted to extract knowledge from the learning process. The
proposed method extracts knowledge from episodes' cumulative rewards. The
proposed method has been evaluated in the Arcade learning environment and the
results indicate an improvement in the learning process in both the single-task
and the multi-task reinforcement learner agents
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Many real-world applications can be described as large-scale games of
imperfect information. To deal with these challenging domains, prior work has
focused on computing Nash equilibria in a handcrafted abstraction of the
domain. In this paper we introduce the first scalable end-to-end approach to
learning approximate Nash equilibria without prior domain knowledge. Our method
combines fictitious self-play with deep reinforcement learning. When applied to
Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium,
whereas common reinforcement learning methods diverged. In Limit Texas Holdem,
a poker game of real-world scale, NFSP learnt a strategy that approached the
performance of state-of-the-art, superhuman algorithms based on significant
domain expertise.Comment: updated version, incorporating conference feedbac
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that
evaluate pairs of agents, for example labeling them `winner' and `loser'. If
the game is approximately transitive, then self-play generates sequences of
agents of increasing strength. However, nontransitive games, such as
rock-paper-scissors, can exhibit strategic cycles, and there is no longer a
clear objective -- we want agents to increase in strength, but against whom is
unclear. In this paper, we introduce a geometric framework for formulating
agent objectives in zero-sum games, in order to construct adaptive sequences of
objectives that yield open-ended learning. The framework allows us to reason
about population performance in nontransitive games, and enables the
development of a new algorithm (rectified Nash response, PSRO_rN) that uses
game-theoretic niching to construct diverse populations of effective agents,
producing a stronger set of agents than existing algorithms. We apply PSRO_rN
to two highly nontransitive resource allocation games and find that PSRO_rN
consistently outperforms the existing alternatives.Comment: ICML 2019, final versio
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