Encourage Deep Q to seek short-term reward I understand that gamma is an important factor in determining the rewards for a deep Q agent, however during testing of my network I am noticing that the agent is outputting more actions to "do nothing" as it learns more about it's given data set (financial stock data).

I have tried tweaking the gamma at different levels ranging from 0 - 1 and everywhere in between, however as the agent continues to learn, the times between actions is getting longer, and longer. This behaviour is undesirable, and it is preferable that the agent be making more often, short-term actions even if they result in reduced reward.

Does anyone have any tips on how to achieve this? Would a minus gamma have adverse effects on the network?

TLDR:
Time between actions becoming increasingly long over time, would prefer an agent that makes many actions over long-term ones.
Thanks for the responses - after taking into account the 'risk' the network experiences when working with unstable financial data I modified the activation layers from RelU to Sigmoid - this has lead to a considerable improvement to both results (profit!) and additionally the rate at which actions are carried out. 

I will also edit the above question to provide further details.