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We propose two new reinforcement learning models: Deep Recurrent Q-Network with Truncated History (T-DRQN) and Deep Attention Recurrent Q-Network with Truncated ...
We propose two new reinforcement learning models: Deep Recurrent Q-Network with Truncated History (T-DRQN) and Deep Attention Recurrent Q-Network with Truncated ...
We propose two new reinforcement learning methods: Deep. Recurrent Q-Network with Truncated History (T-DRQN(n)) and Deep Attention Recurrent Q-Network with ...
This paper introduces a theoretical framework for studying the behaviour of RL algorithms that learn to control an MDP using history-based feature abstraction ...
Bibliographic details on Deep Recurrent Q-Network with Truncated History.
[PDF] Deep Attention Recurrent Q-Network - Semantic Scholar
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Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN.
The resulting Deep Recurrent Q-Network (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through ...
Missing: Truncated | Show results with:Truncated
Jul 23, 2015 · This article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a ...
Missing: Truncated | Show results with:Truncated
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Nov 5, 2024 · This paper proposes a new interpretation on how to sample from a buffer of data to avoid the well known trade-offs of truncated BPTT. The final ...
In this paper, we present a PSR model-based DQN approach which combines the strengths of the PSR model and DQN planning. We use a recurrent network to establish ...