--- a
+++ b/main.py
@@ -0,0 +1,70 @@
+from SAC.RL_Framework_Mujoco import Muscle_Env    #Muscle_Env is the musculoskeletal environment
+from simulate import Simulate
+import config
+import pdb
+
+def main():
+
+    """ Train an agent to control a musculoskeletal system using DRL and RNNs
+        ---------------------------------------------------------------------
+    * Simulate object runs training
+    * Specify whether testing or not in config file, along with the preferred file to save testing statistics
+    * To ensure model is saved properly, specify your preferred directories for storing state_dict
+    * A list of possible commands with their functions is given in the config.py file as well as the simulate.py file
+    * Specify kinematics by choosing kinematics_path in config file, monkey data is currently provided by default
+    """
+
+    ### PARAMETERS ###
+    parser = config.config_parser()
+    args = parser.parse_args()
+
+    ### TRAINING OBJECT ###
+    trainer = Simulate(Muscle_Env, args)
+
+    # trainer = Simulate(
+    #     Muscle_Env,
+    #     args.model,
+    #     args.gamma,
+    #     args.tau,
+    #     args.lr,
+    #     args.alpha,
+    #     args.automatic_entropy_tuning,
+    #     args.seed,
+    #     args.policy_batch_size,
+    #     args.hidden_size,
+    #     args.policy_replay_size,
+    #     args.multi_policy_loss,
+    #     args.batch_iters,
+    #     args.cuda,
+    #     args.visualize,
+    #     args.root_dir,
+    #     args.checkpoint_file,
+    #     args.checkpoint_folder,
+    #     args.statistics_folder,
+    #     args.total_episodes,
+    #     args.load_saved_nets_for_training,
+    #     args.save_iter,
+    #     args.mode,
+    #     args.musculoskeletal_model_path,
+    #     args.initial_pose_path,
+    #     args.kinematics_path,
+    #     args.nusim_data_path,
+    #     args.condition_selection_strategy,
+    #     args.sim_dt,
+    #     args.frame_repeat,
+    #     args.n_fixedsteps,
+    #     args.timestep_limit,
+    #     args.trajectory_scaling,
+    #     args.center
+    # )
+
+    ### TRAIN OR TEST ###
+    if args.mode == "train":
+        trainer.train()
+    elif args.mode in ["test", "SFE", "sensory_pert", "neural_pert", "musculo_properties"]:
+        trainer.test(args.test_data_filename)
+    else:
+        raise NotImplementedError
+
+if __name__ == '__main__':
+    main()