パラメータ空間ノイズによる探索
Parameter Space Noise for Exploration
強化学習の探索において、行動空間ではなくエージェントのパラメータにノイズを加える手法を提案し、DQN・DDPG・TRPOで従来の行動空間ノイズや進化戦略より効率的に学習できることを示した。
著者: Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz
分類: cs.LG, cs.AI, cs.NE, cs.RO, stat.ML
原文アブストラクト
Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.
