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Random Curiosity-driven Exploration in Deep Reinforcement Learning
کاوش اکتشاف محور کنجکاوی تصادفی در یادگیری تقویتی عمیق-2020 Reinforcement learning (RL) depends on carefully engineering environment
rewards. However, rewards from environments are extremely sparse for many
RL tasks, challenging for the agent to learn skills and interact with the environment.
One solution to this problem is to create intrinsic rewards for
agents and to make rewards dense and more suitable for learning. Recent algorithms,
such as curiosity-driven exploration, usually estimate the novelty
of the next state through the prediction error of dynamics models. However,
these methods are typically limited by the capacity of their dynamics
models. In this paper, a random curiosity-driven model using deep reinforcement
learning is proposed, which uses a target network with fixed weights to
maintain the stability of dynamics models and create more suitable intrinsic
rewards. We integrate the parametric exploration method for further promoting
sufficient exploration. Besides, a deeper and more closely connected
network is utilized for encoding the pixel images for policy-gradient. By comparing
our method against the previous approaches in several environments,
the experiments show that our method achieves state-of-the-art performance
on most but not all of the Atari games. Keywords: Deep reinforcement learning | curiosity-driven exploration | intrinsic rewards |
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