Code: https://github.com/google-research/google-research/tree/master/munchausen_rlInternational audienceBootstrapping is a core mechanism in Reinforcement Learning (RL). Most algorithms, based on temporal differences, replace the true value of a transiting state by their current estimate of this value. Yet, another estimate could be leveraged to bootstrap RL: the current policy. Our core contribution stands in a very simple idea: adding the scaled log-policy to the immediate reward. We show that slightly modifying Deep Q-Network (DQN) in that way provides an agent that is competitive with distributional methods on Atari games, without making use of distributional RL, n-step returns or prioritized replay. To demonstrate the versatility of th...
Deep reinforcement learning (DRL) systems have transformed artificial intelligenceby solving complex...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
© Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved. Combining d...
In reinforcement learning (RL), an agent interacts with the environment by taking actions and observ...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It...
In this work, we build recent advances in distributional reinforcement learning to give a state-of-a...
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-m...
This thesis tests the hypothesis that distributional deep reinforcement learning (RL) algorithms get...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In t...
Games for the Atari 2600 console provide great environments for testing reinforcement learning algor...
We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this me...
Reinforcement learning can be compared to howhumans learn – by interaction, which is the fundamental...
Deep reinforcement learning (DRL) systems have transformed artificial intelligenceby solving complex...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
© Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved. Combining d...
In reinforcement learning (RL), an agent interacts with the environment by taking actions and observ...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It...
In this work, we build recent advances in distributional reinforcement learning to give a state-of-a...
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-m...
This thesis tests the hypothesis that distributional deep reinforcement learning (RL) algorithms get...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In t...
Games for the Atari 2600 console provide great environments for testing reinforcement learning algor...
We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this me...
Reinforcement learning can be compared to howhumans learn – by interaction, which is the fundamental...
Deep reinforcement learning (DRL) systems have transformed artificial intelligenceby solving complex...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
© Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved. Combining d...