Introduction finnished

project_proposal.tex

@@ -11,13 +11,14 @@
 
 \begin{document}
 \maketitle
+\tableofcontents
 
 \section{Introduction}
 I want to build a reinforcement learning project about single asset stock trading. First I
 want to start a simple environment with just the actions buy and sell. For the reward function I
 also want to keep it simple at first by just using the profit as reward.
 
-In contrast to the algorithms we already heard in the lector I have to try out deep
+In contrast to the algorithms we already heard in the lecture, I have to try out deep
 reinforcement learning algorithms because the price is a continuous variable. In theory, you could
 model the price with a specific resolution with many states. However, this can very quickly become
 impractical for classic reinforcement learning methods. Also, deep reinforcement learning can 
@@ -25,11 +26,29 @@ recognize pattern to act good in previously unseen states.
 
 I want to try out different reinforcement learning algorithms to see with works best for the trading
 environment. First I want to try out the Deep-Q-Network algorithm. It predicts the Q-function and
-uses Elpsilon-Greedy Exploration. I plan to try out different formulas for the epsilon decay.
-Because DQN often overestimates the Q-values. It uses two networks for updating the policy. The
+uses Epsilon-Greedy Exploration. I plan to try out different formulas for the epsilon decay.
+
+Because DQN often overestimates the Q-values, I want to try out a variation of DQN, called Double
+DQN. It uses two networks for updating the policy. The
 online network selects the action with the highest Q-value and the target network evaluates the
 action. This causes more stable and better learning. I will try, if Double DQN will improve the
-results. Then I want to try out the Proximal Policy Optimization algorithm.
+results. At last, I want to try out the Proximal Policy Optimization algorithm.
 
-I find it also very interesting, if providing the RL agent with additional information. 
+After implementing these different algorithms, I need to train these and compare the
+results.
+
+I find it also very interesting, if providing the RL agent with additional information then just the
+price, positively impacts the results. For example, I can add technical indicators, market volume or
+an online news score about the company. The last one is probably a bit difficult because you need a
+LLM which gives web scrapped articles a score how good the news is for a company. After adding this
+information, I need to reevaluate which algorithm is the best.
+
+\section{Libraries and Tools}
+
+\section{Development plan}
+
+\section{Availability}
+I am on vacation from the 04.08 to 13.08. On the 15. I am on an event, but I have time on the 14.
+From the 18. onwards I am available for the next couple of weeks. I look forward to the
+presentation, and thank you for giving me the additional time.
 \end{document}