Add to ORKGIn reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding invariances from raw pixels. Nevertheless, we empirically find that not all samples are equally important and hence simply injecting more augmented inputs may instead cause instability in Q-learning. In this paper, we approach this problem systematically by developing a model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF), which can fully exploit sample importance and improve learning efficiency in a self-supervised manner. Facilitated by the proposed contrastive curiosity, CCLF is capable of prioritizing the experience replay, selecting the most ...
The application of reinforcement learning to problems with continuous domains requires rep-resenting...
The development of reinforcement learning attracts more and more attention among researchers. Levera...
Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to in...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
The practical application of learning agents requires sample efficient and interpretable algorithms....
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedi...
In the search for more sample-efficient reinforcement-learning (RL) algorithms, a promising directio...
Reinforcement learning (RL) is a general framework for learning and evaluating intelligent behaviors...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visua...
Model-based reinforcement learning (RL) methods are appealing in the offline setting because they al...
Recent progress in reinforcement learning (RL) has started producing generally capable agents that c...
Reinforcement Learning (RL) has seen exponential performance improvements over the past decade, achi...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
The application of reinforcement learning to problems with continuous domains requires rep-resenting...
The development of reinforcement learning attracts more and more attention among researchers. Levera...
Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to in...
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward ...
The practical application of learning agents requires sample efficient and interpretable algorithms....
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedi...
In the search for more sample-efficient reinforcement-learning (RL) algorithms, a promising directio...
Reinforcement learning (RL) is a general framework for learning and evaluating intelligent behaviors...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visua...
Model-based reinforcement learning (RL) methods are appealing in the offline setting because they al...
Recent progress in reinforcement learning (RL) has started producing generally capable agents that c...
Reinforcement Learning (RL) has seen exponential performance improvements over the past decade, achi...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
The application of reinforcement learning to problems with continuous domains requires rep-resenting...
The development of reinforcement learning attracts more and more attention among researchers. Levera...
Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to in...