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rl's Introduction

Matt McDermott Comp 138: Reinforcement Learning

Part 1: Direct Friction Identification

cd final

<python viz2.py> - compares trajectories of "ground truth" and best estimated parameters

/tests/simplified_model/1link - graveyard of failed attempts at parameter estimation

Part 2: Torque control

cd torque_control

<python viz.py> #Runs OpenGL visualization of arm attempting to reach position setpoints from random initial configuration
				# 	runs off of specified "path.npy" and "goal_path.npy" obtained from: 
					/tests/3DOF/gen_path_from_policy_variable_goal.py


/tests/3dof/
<python main_with_variable_goal.py> #trains model with random starting states, generates actor and critic checkpoints saved in 
										/checkpoints/ dir

/tests/3dof/
<python view_results.py> view actor loss, critic loss, and reward plots

/tests/3dof/
<python gen_path_from_policy.py> generates path of arm for viz basd on random inputs and the policy generated by the main func 					stored in the /checkpoints/ directory

/demo/ - videos and GIFs of different tests run on various policies

/tests/3DOF/ solve_EOM_combined_friction_model.py - solves EOM and saves as func to binary file

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