Jun 6, 2017 · Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies ...
Nov 13, 2017 · Parameter space noise allows reinforcement learning algorithms to explore by perturbing parameters instead of actions, often leading to significantly improved ...
Jul 27, 2017 · Parameter noise helps algorithms explore their environments more effectively, leading to higher scores and more elegant behaviors.
This paper investigates how parameter space noise can be effectively combined with off-the-shelf deep. RL algorithms such as DQN (Mnih et al., 2015), DDPG ( ...
This work demonstrates that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies ...
This paper investigates how parameter space noise can be effectively combined with off-the-shelf deep RL algorithms such as DQN [13], DDPG [14], and TRPO [15] ...
May 17, 2019 · Noisy nets add additional noise parameters that are optimized through gradient descent rather than using a scalar standard deviation and a scaling factor.
View recent discussion. Abstract: Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action ...
Apr 22, 2024 · What is I in the noise described in the paper "Parameter Space Noise for Exploration"? 0 · Which is more important, doubt or reinforcement? 5.
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Nov 30, 2017 · Parameter space noise is conceptually simple yet allows state-of-the-art algorithms to learn in environments in which traditional action space ...