Add to ORKGReinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which is increasingly involved in smart manufacturing and autonomous driving. However, RL consistently suffers from the exploration-exploitation dilemma. In this paper, we investigated the problem of improving exploration in RL and introduced the intrinsically-motivated RL. In sharp contrast to the classic exploration strategies, intrinsically-motivated RL utilizes the intrinsic learning motivation to provide sustainable exploration incentives. We carefully classified the existing intrinsic reward methods and analyzed their practical drawbacks. Moreover, we proposed a ne...
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative rew...
One of the most critical challenges in deep reinforcement learning is to maintain the long-term expl...
In the last few years, the research activity around reinforcement learning tasks formulated over env...
We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-...
The reinforcement learning (RL) research area is very active, with an important number of new contri...
In the last few years we have experienced great advances in the field of reinforcement learning (RL)...
Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we ...
Despite the great potential of reinforcement learning (RL) in solving complex decision-making proble...
Abstract — Motivation is a key factor in human learning. We learn best when we are highly motivated ...
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal...
Abstract. In this paper, we propose an adaptation of four common appraisal dimensions that evaluate ...
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative rew...
One of the most critical challenges in deep reinforcement learning is to maintain the long-term expl...
In the last few years, the research activity around reinforcement learning tasks formulated over env...
We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-...
The reinforcement learning (RL) research area is very active, with an important number of new contri...
In the last few years we have experienced great advances in the field of reinforcement learning (RL)...
Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we ...
Despite the great potential of reinforcement learning (RL) in solving complex decision-making proble...
Abstract — Motivation is a key factor in human learning. We learn best when we are highly motivated ...
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal...
Abstract. In this paper, we propose an adaptation of four common appraisal dimensions that evaluate ...
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative rew...