arXiv:2003.06497
공식 코드 · Papers with Code
Deep Deterministic Portfolio Optimization
원문 초록
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.
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Deep Reinforcement Learning with Gradient Eligibility Traces
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Task Independent Capsule-Based Agents for Deep Q-Learning
A State Representation Dueling Network for Deep Reinforcement Learning
Drafting in Collectible Card Games via Reinforcement Learning
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