Deep reinforcement learning (DRL) algorithm is often used to find the best trading strategy in algorithmic trading. However, the classical DRL model is difficult to achieve rapid convergence, and the ...

While ChatGPT is easy to use on the surface, many complex computations that are customized to each user are happening behind ...

Kabbani, T. and Duman, E. (2022) Deep Reinforcement Learning Approach for Trading Automation in the Stock Market. IEEE Access, 10, 93564-93574.

Cryptocurrencies have a high volatility but also provide a high potential of profit. This paper discusses the use of deep reinforcement learning in bitcoin trading aiming to optimize profit. Various ...

Stock trading is a continuous process of testing new ideas, getting feedback from the market, and trying to optimize trading strategies over time. We can model the stock trading process as the Markov ...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

In this paper, a deep reinforcement learning-based bidding strategy is proposed for buyers and sellers in the peer-to-peer energy trading market, in which the bidding strategy with sequential decision ...

The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal ...