Add to ORKGWe present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL). More specifically, AIRS selects shaping function from a predefined set based on the estimated task return in real-time, providing reliable exploration incentives and alleviating the biased objective problem. Moreover, we develop an intrinsic reward toolkit to provide efficient and reliable implementations of diverse intrinsic reward approaches. We test AIRS on various tasks of Procgen games and DeepMind Control Suite. Extensive simulation demonstrates that AIRS can outperform the benchmarking schemes and achieve superior performance with simple architecture.Comme...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
International audienceDespite their apparent importance for the acquisition of fullfledged human int...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstr...
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
Abstract: In Reinforcement Learning, Intrinsic Motivation motivates directed behaviors through a wid...
In the last few years, the research activity around reinforcement learning tasks formulated over env...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
In the last few years we have experienced great advances in the field of reinforcement learning (RL)...
One of the most critical challenges in deep reinforcement learning is to maintain the long-term expl...
Deep reinforcement learning has become one of the hottest research topics in machine learning. In re...
Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement le...
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One commo...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
International audienceDespite their apparent importance for the acquisition of fullfledged human int...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstr...
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
Abstract: In Reinforcement Learning, Intrinsic Motivation motivates directed behaviors through a wid...
In the last few years, the research activity around reinforcement learning tasks formulated over env...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
In the last few years we have experienced great advances in the field of reinforcement learning (RL)...
One of the most critical challenges in deep reinforcement learning is to maintain the long-term expl...
Deep reinforcement learning has become one of the hottest research topics in machine learning. In re...
Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement le...
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One commo...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
International audienceDespite their apparent importance for the acquisition of fullfledged human int...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...