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Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Naturalistic reinforcement learning
Humans possess a remarkable ability to make decisions within real-world environments that
are expansive, complex, and multidimensional. Human cognitive computational …
are expansive, complex, and multidimensional. Human cognitive computational …
A survey of meta-reinforcement learning
J Beck, R Vuorio, EZ Liu, Z ** rewards: A new approach of reward sha**
Reward sha** is an effective technique for incorporating domain knowledge into
reinforcement learning (RL). Existing approaches such as potential-based reward sha** …
reinforcement learning (RL). Existing approaches such as potential-based reward sha** …
On the expressivity of markov reward
Reward is the driving force for reinforcement-learning agents. This paper is dedicated to
understanding the expressivity of reward as a way to capture tasks that we would want an …
understanding the expressivity of reward as a way to capture tasks that we would want an …
Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning
Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …
Discovering reinforcement learning algorithms
Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …
several possible rules, discovered manually through years of research. Automating the …
Exploration in deep reinforcement learning: From single-agent to multiagent domain
Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …
have achieved significant success across a wide range of domains, including game artificial …