To analyze the performance of various RL algorithms on different games such as Minesweeper, Slither.io and Reconnaissance Blind Chess.
By working on multiplayer environments and incomplete information problems, we intend to find improvements of the current state-of-the-art methods which can find applications in sophisticated problems such as robotics and autonomous driving.
- The agent is made to interact with an emulated game environment. In case of slither, the OpenAI universe package is used to create a container image of the online version of the game while in case of Minesweeper, a pygame environment is used.
- We stack 4-6 images as one training input to add a sense of direction to the game and pass this image to a CNN followed by DENSE LAYERED NEURAL NETWORK. The output is value of the different states or policy depending on the algorithm. (Value of a state tells how good or bad the state/snapshot/frame of the game is, and policy is the strategy based on which the bot takes actions)
- The agent is trained using Q-value based and Policy based methods such as Deep Q-learning, Policy gradients and Actor-Critic methods (to get the best of both worlds)
- The effects of reward shaping, priority experience replay queues, recurrent and LSTM memory layers (in case of Partially Observable MDPs) etc. on the performance of the agent are analyzed.
- Document the training and compare the success of the different algorithms