This is the accompannying code for the paper "SLAM-Safe Planner: Preventing Monocular SLAM Failure using Reinforcement Learning" and "Data driven strategies for Active Monocular SLAM using Inverse Reinforcement Learning" To run the code, download this repository and a modified version of PTAM from https://github.com/souljaboy764/ethzasl_ptam/ to your catkin workspace and compile it. For running the agent on maps: In the turtlebot_gazebo.launch change the argument "world_file" to the corresponding map world file (map1.world, map2.world, map3.world, corridor.world or rooms.world) and set the corresponding initial positions in joystick.launch Open 4 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav joystick.launch Terminal 4: rosrun rviz rviz -d `rospack find rl_nav`/ptam.rviz Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, give an intermediate point using the "2D Pose Estimate" button in rviz and give the goal location using "2D Nav Goal" For traning the agent, In the turtlebot_gazebo.launch change the argument "world_file" to training.world Open 3 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav train.launch Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, press the "A" button on the xbox controller to start training. For testing the agent on steps to breakage, In the turtlebot_gazebo.launch change the argument "world_file" to training.world Open 3 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav test.launch Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, press the "A" button on the xbox controller to start testing. For running the IRL agent, just change the weights in qMatData.txt to the weights in qMatData_SGD.txt and run any of the above. For training the IRL agent, run IRLAgent.py with the data from https://www.dropbox.com/s/qnp8rs92kbmqz1e/qTrain.txt?dl=0 in the same folder as IRLAgent.py, which will save the final Q values in qRegressor.pkl