Last fine tuning

src/main.py

@@ -14,10 +14,7 @@
 from cli import run_cli_cmnds
 
 
-def main():
-    model_name = "blitz5k"
-    model = neural_net.load(f"../models/{model_name}.pt")
-
+def test():
     # env = discrete_env_with_nn(rf.right, model)
     # from gym_cartpole_swingup.envs import cartpole_swingup
     # env = cartpole_swingup.CartPoleSwingUpV1()
@@ -29,33 +26,27 @@ def main():
     input("continue?")
     histories = agents.run(ppo, env, 10)
 
-    with open("history_ppo.p", "wb") as f:
-        dill.dump(histories, f)
-
-    evaluation.plot_angles(histories[0], model_name)
+    evaluation.plot_angles(histories[0], "no model")
 
 
-def test2():
+def main():
     model_name = "blitz5k"
     model = neural_net.load(f"../models/{model_name}.pt")
 
     env = neural_net.USUCEnvWithNN.create(model, rf.best, "../discrete-usuc-dataset")
-    env.reset(1)
-    history = utils.random_actions(env)
-    evaluation.plot_angles(history, model_name)
-    evaluation.plot_reward_angle(history)
 
+    ppo = agents.create("ppo", env)
+    agents.train(ppo, total_timesteps=10000)
+    agents.save(ppo, "../agents/ppo")
 
-def analysis():
-    # load history for analysis
-    with open("history_ppo.p", "rb") as f:
-        history = dill.load(f)
+    input("continue?")
+    histories = agents.run(ppo, env, 10)
 
-    evaluation.plot_reward_angle(history)
+    evaluation.plot_angles(histories[0], model_name)
+    evaluation.plot_reward_angle(histories[0])
 
 
 if __name__ == "__main__":
-    test2()
+    # test()
     # main()
-    # analysis()
     run_cli_cmnds()