Mar 2, 2001 · In this paper, we explore a {\it stigmergic} approach, in which the agent's actions include the ability to set and clear bits in an external ...
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Jan 22, 2020 · Why is it difficult? - Basic RL (i.e. Q-learning) can perform poorly in partially observable domains. - Due to strong Markov assumptions.
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This paper studies a lightweight approach to tackle partial observability in RL by providing the agent with an external memory and additional actions.
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In this thesis, we will develop a novel method, called online policy gradient over a reservoir (OPGOR), for selecting what to remember from the stream of ...
Abstract. We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware ...
External memory algorithms or out-of-core algorithms are algorithms that are designed to process data that are too large to fit into a computer's main memory ...
Missing: Learning Policies
Memory Based RL An external memory buffer enables the storage and usage of past experiences to improve RL algorithms. Episodic reinforcement learning ...
In order to explore and navigate effectively, the policy is conditioned on an external memory module that remembers useful information from the current episode.
Oct 5, 2020 · In this paper, we study a lightweight approach to tackle partial observability in RL. We provide the agent with an external memory and additional actions.