Add to ORKGOne problem of current Reinforcement Learning algorithms is finding a balance between exploitation of existing knowledge and exploration for a new experience. Curiosity exploration bonus has been proposed to address this problem, but current implementations are vulnerable to stochastic noise inside the environment. The new approach presented in this thesis utilises exploration bonus based on the predicted novelty of the next state. That protects exploration from noise issues during training. This work also introduces a new way of combining extrinsic and intrinsic rewards. Both improvements help to overcome a number of problems that Reinforcement Learning had until now
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are...
In reinforcement learning (RL), a decision maker searching for the most rewarding option is often fa...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
Exploration is curiosity-driven when it relies on the intrinsic motivation to know rather than on ex...
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed...
A fundamental challenge for reinforcement learning (RL) is how to achieve effcient exploration in in...
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
In many reinforcement learning scenarios such as many game environments or real lifesituations, the ...
Institute of Perception, Action and BehaviourRecently there has been a good deal of interest in usin...
The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in ...
International audienceRealistic environments often provide agents with very limited feedback. When t...
Applying reinforcement learning techniques to real-world problems as well as long standing challenge...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
Model-Based Reinforcement Learning (MBRL) can greatly profit from using world models for estimating ...
This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based...
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are...
In reinforcement learning (RL), a decision maker searching for the most rewarding option is often fa...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
Exploration is curiosity-driven when it relies on the intrinsic motivation to know rather than on ex...
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed...
A fundamental challenge for reinforcement learning (RL) is how to achieve effcient exploration in in...
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
In many reinforcement learning scenarios such as many game environments or real lifesituations, the ...
Institute of Perception, Action and BehaviourRecently there has been a good deal of interest in usin...
The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in ...
International audienceRealistic environments often provide agents with very limited feedback. When t...
Applying reinforcement learning techniques to real-world problems as well as long standing challenge...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
Model-Based Reinforcement Learning (MBRL) can greatly profit from using world models for estimating ...
This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based...
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are...
In reinforcement learning (RL), a decision maker searching for the most rewarding option is often fa...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...