  In today's forex market traders increasingly turn to algorithmic trading,
leveraging computers to seek more profits. Deep learning techniques as
cutting-edge advancements in machine learning, capable of identifying patterns
in financial data. Traders utilize these patterns to execute more effective
trades, adhering to algorithmic trading rules. Deep reinforcement learning
methods (DRL), by directly executing trades based on identified patterns and
assessing their profitability, offer advantages over traditional DL approaches.
This research pioneers the application of a multi-agent (MA) RL framework with
the state-of-the-art Asynchronous Advantage Actor-Critic (A3C) algorithm. The
proposed method employs parallel learning across multiple asynchronous workers,
each specialized in trading across multiple currency pairs to explore the
potential for nuanced strategies tailored to different market conditions and
currency pairs. Two different A3C with lock and without lock MA model was
proposed and trained on single currency and multi-currency. The results
indicate that both model outperform on Proximal Policy Optimization model. A3C
with lock outperforms other in single currency training scenario and A3C
without Lock outperforms other in multi-currency scenario. The findings
demonstrate that this approach facilitates broader and faster exploration of
different currency pairs, significantly enhancing trading returns.
Additionally, the agent can learn a more profitable trading strategy in a
shorter time.
