Add to ORKGFor reinforcement learning (RL) algorithms, the sparsity of reward has always been a problem to be solved. Because reinforcement learning cannot get effective feedback in most cases, the agent is difficult to learn effectively. We propose a model-based algorithm to create extra rewards and increase reward density to make it easy to learning. To reshape rewards, we use model error as an extra reward and add it to the return. We test our approach in the Google Research Football Environment, and our algorithm gets good results
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex...
Applying conventional reinforcement to complex domains requires the use of an overly simplified task...
Uploaded version with minor typographic errors corrected, per request of the Grad office, 12/13/2022...
In this master thesis, we have tried to solve two of most prominent Reinforcement Learning problems:...
In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance...
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of...
Reinforcement learning involves the study of how to solve sequential decision-making problems using ...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy sc...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Solving sparse-reward environments is one of the most considerable challenges for state-of-the-art ...
Thesis (Ph.D.)--University of Washington, 2021Rapid strides made in the development of computing inf...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
For many problems which would be natural for reinforcement learning, the reward signal is not a sing...
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex...
Applying conventional reinforcement to complex domains requires the use of an overly simplified task...
Uploaded version with minor typographic errors corrected, per request of the Grad office, 12/13/2022...
In this master thesis, we have tried to solve two of most prominent Reinforcement Learning problems:...
In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance...
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of...
Reinforcement learning involves the study of how to solve sequential decision-making problems using ...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy sc...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Solving sparse-reward environments is one of the most considerable challenges for state-of-the-art ...
Thesis (Ph.D.)--University of Washington, 2021Rapid strides made in the development of computing inf...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
For many problems which would be natural for reinforcement learning, the reward signal is not a sing...
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex...
Applying conventional reinforcement to complex domains requires the use of an overly simplified task...