1. cs18s038_PA1 Course Project

   • Implementation of epsilon-greedy and related plots.
   • Implementation of soft-max algorithm and related plots.
   • Implementation of UCB1 algorithm and comparing with epsilon-greedy and soft-maxand related plots.
   • Implementation of Median algorithm and comparing with epsilon-greedy, soft-maxand UCB1 and related plots.
   • Comparison of the above four algorithm as the number of arm grows.

   Note: For more detailed analysis of each observation and inferernce drawn from it, Please have a look at full report Click here.

2. cs18s038_PA2 Course Project

   PUDDLE WORLD!

   

   • Implementation of Sarsa for different goals and related plots in puddle world.
   • Implementation of Q-Learning for different goals and related plots in puddle world.
   • Implementation of Sarsa Lambda for different goals and related plots in puddle world.

   

   • Implementation of Policy gradient on above environments. Experiment involves-

     • Hyper parameter tuning.
     • Value function visualisation.
     • Trajectory and policy.
     • Inference from observation .

     Note: For more detailed analysis of each observation and inferernce drawn from it, Please have a look at full report Click here.

3.cs18s038_PA3 Course Project

• Implementation of SMDP-Q Learning.
• Implementation of Intra option Q-Learning.
• Visualisation of Q-values.
• Visualisation of V-values.
• Analysis of Observations and Inference

Problem solved using DQN model. Experiment involves-

• Implementation of DQN and related plots.
• Best hyperparameters.
• Observations and inference.
• Playing with Replay memory and Target network .

Note: For more detailed analysis of each observation and inferernce drawn from it, Please have a look at full report Click here.