  Deep Reinforcement Learning solutions have been applied to different control
problems with outperforming and promising results. In this research work we
have applied Proximal Policy Optimization, Soft Actor-Critic and Generative
Adversarial Imitation Learning to strategy design problem of three
cryptocurrency markets. Our input data includes price data and technical
indicators. We have implemented a Gym environment based on cryptocurrency
markets to be used with the algorithms. Our test results on unseen data shows a
great potential for this approach in helping investors with an expert system to
exploit the market and gain profit. Our highest gain for an unseen 66 day span
is 4850 US dollars per 10000 US dollars investment. We also discuss on how a
specific hyperparameter in the environment design can be used to adjust risk in
the generated strategies.
