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+# RL_Library
+
+Nowadays, artificial intelligence is covers an important role in industry and
+scientific research. Next to clustering, deep learning and neural networks;
+reinforcement learning is becoming more and more popular. In the present
+work, the performance of reinforcement learning algorithms has been tested.
+Further more, two types of results have been gathered:
+- A solo-agent version, in which algorithms are executed as usual in the
+given environment.
+- A cooperative version, in which two or more algorithms work together
+in order to take decisions.
+
+## Analysed algorithms
+
+- Q-Learning
+- SARSA
+- DQN/DDQN
+- AC (not fully tested)
+
+## Ensembling strategies
+
+- Major voting based
+- Rank voting based
+- Trust based
+
+## OpenAI Gym
+OpenAI Gym is a toolkit for developing and comparing reinforcement learning
+algorithms written in python. It provides a set of environments ranging
+from simple textual games to emulated Atari games and physics problems.
+Each environment is shipped with a set of possible actions/moves with a
+related reward. The user has the possibility to obtain a standardised set of
+environments in order to feed the reinforcement learning algorithm. Moreover,
+an optional rendering is provided in order to offer a clear view of what
+is happening in background. There are different types of environments, characterised
+by different features such as:
+- Observation space domain: discrete or continuous.
+- Observation state type: memory representation or video frame.
+- Reward range: finite or infinite set of values.
+- Steps limitation.
+- Maximum number of trials.
+
+### Testing environments
+- Frozen-Lake4x4
+- Frozen-Lake8x8
+- Taxi
+- MountainCar
+- Breakout (not fully tested)
+- Pong (not fully tested)
+- CartPole