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README.md

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 # TCP-RL
 TCP congestion control using reinforcement learning
+
+## Tools
+The following RL and networking toolsets are used for the implementation of the project.
+
+### ns-3
+ns-3 is a discrete-event network simulator for networking systems, primarily used for research and education. It became a standard in networking research in recent years as the results obtained are accepted by the science community. In this project, we used NS-3 to generate traffic and also simulate our environment.
+See: https://www.nsnam.org
+
+### Gym
+Gym is an open source Python library, developed by OpenAI, for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Since its release, Gym's API has become the field standard for doing this.
+See: https://gym.openai.com/
+
+### ns3-gym
+ns3-gym is a framework that integrates both OpenAI's Gym and ns-3 in order to encourage usage of reinforcement learning in networking research. ns3-gym works as a proxy between the ns-3 environment simulator, coded in C++, and the RL agent, coded in Python.
+See: https://github.com/tkn-tub/ns3-gym
+
+## Agent
+The agent interacts with the network environment and keeps exploring the optimal policy by taking various actions such as increasing or decreasing congestion window size. The environment setup consists of a combination of the ns-3 network simulator, ns3-gym proxy, and a Python based RL agent. Our mapping approach is as follows:
+
+* State: The state space is the profile of the networking simulation that is provided by the ns-3 simulator. It includes attributes such as congestion window size, segment size, segments acknowledged, average round trip time (RTT), and other such connection parameters.
+* Action: The actions are configured to increase, decrease or maintain the congestion window size and slow start threshold by certain degrees based on fine-tuning.
+* Reward: The reward is a utility function that considers the average RTT of each step as the defining parameter. It allocates the reward depending on the trend in the parameter.
+
+## Installation and testing
+
+* Install Python
+* Install and build ns3-gym (this includes ns-3 and dependencies)
+* Install Tensorflow and other agent dependencies
+* Build the project
+
+Then, in two terminals, run:
+```
+# Terminal 1:
+./waf --run "TCP-RL --transport_prot=TcpRlTimeBased"
+
+# Terminal 2:
+./TCP-RL-Agent.py --start=0
+```