Add to ORKGAlthough Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal. To improve learning efficiency for such tasks, this paper proposes a DRL exploration technique, termed A^2, which integrates two components inspired by human experiences: Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a complex task into subtasks, and then provides the correct orders of subtasks to learn. During training, the agent explores the environment adaptively, acting more deterministically for well-mastered subtasks and more st...
Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL...
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved s...
In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). How...
Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, ...
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little t...
Deep reinforcement learning (DRL) has made great progress in dealing with complex control problems i...
Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions with the e...
Deep Reinforcement Learning (DRL) is a machine learning paradigm which uses deep neural networks as ...
Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However...
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of...
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
Simple day-to-day activities like picking up or reaching out to an object seem easy for a human, but...
Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the des...
Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL...
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved s...
In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). How...
Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, ...
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little t...
Deep reinforcement learning (DRL) has made great progress in dealing with complex control problems i...
Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions with the e...
Deep Reinforcement Learning (DRL) is a machine learning paradigm which uses deep neural networks as ...
Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However...
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of...
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
Simple day-to-day activities like picking up or reaching out to an object seem easy for a human, but...
Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the des...
Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL...
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved s...
In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). How...