diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..9478db48e881240987301c71b97f727996fa6db0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+
+.idea/misc.xml
diff --git a/.idea/.gitignore b/.idea/.gitignore
index 13566b81b018ad684f3a35fee301741b2734c8f4..a9d7db9c0a81b2db47ca92e4e180b30090b27632 100644
--- a/.idea/.gitignore
+++ b/.idea/.gitignore
@@ -6,3 +6,5 @@
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
+# GitHub Copilot persisted chat sessions
+/copilot/chatSessions
diff --git a/.idea/Ion_ai1.iml b/.idea/Ion_ai1.iml
index 74d515a027de98657e9d3d5f0f1831882fd81374..29456f15b21f52aa68d3fd664153a044014ef552 100644
--- a/.idea/Ion_ai1.iml
+++ b/.idea/Ion_ai1.iml
@@ -2,6 +2,7 @@
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
+ <excludeFolder url="file://$MODULE_DIR$/.idea/copilot/chatSessions" />
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="inheritedJdk" />
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index ea26b4d402d793a4a3def4e8ebe7d9aa86654d0e..0000000000000000000000000000000000000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
- <component name="Black">
- <option name="sdkName" value="Python 3.11 (Ion_ai1)" />
- </component>
- <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (Ion_ai1)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/.idea/other.xml b/.idea/other.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2e75c2e2af6fe9ab3294dedf305ebb894bb324d7
--- /dev/null
+++ b/.idea/other.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="PySciProjectComponent">
+ <option name="PY_INTERACTIVE_PLOTS_SUGGESTED" value="true" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/Ionsmaxes1710952880_torch.csv b/Ionsmaxes1710952880_torch.csv
deleted file mode 100644
index bbdd7198037e81e00d121439429155a3fa28e352..0000000000000000000000000000000000000000
--- a/Ionsmaxes1710952880_torch.csv
+++ /dev/null
@@ -1,62 +0,0 @@
-,Stepcount,IonVal
-0,1.0,0.0029939040000403
-1,1.0,0.0029939040000403
-2,1.0,0.0029939040000403
-3,1.0,0.0029939040000403
-4,1.0,0.0029939040000403
-5,6.0,1.0932635808000255
-6,46.0,5.460549600000039
-7,38.0,5.460549600000039
-8,67.0,5.460549600000039
-9,26.0,5.460549600000039
-10,12.0,5.460549600000039
-11,242.0,5.657124489600028
-12,40.0,5.460549600000039
-13,129.0,5.657124489600028
-14,15.0,5.460549600000039
-15,16.0,5.460549600000039
-16,111.0,5.657124489600028
-17,54.0,5.657124489600028
-18,124.0,5.657124489600028
-19,200.0,5.657124489600028
-20,238.0,5.657124489600028
-21,10.0,5.460549600000039
-22,11.0,5.460549600000039
-23,13.0,5.460549600000039
-24,420.0,5.657124489600028
-25,11.0,5.460549600000039
-26,14.0,5.460549600000039
-27,11.0,5.460549600000039
-28,356.0,5.657124489600028
-29,13.0,5.460549600000039
-30,11.0,5.460549600000039
-31,8.0,5.460549600000039
-32,12.0,5.460549600000039
-33,9.0,5.460549600000039
-34,9.0,5.460549600000039
-35,247.0,5.657124489600028
-36,11.0,5.460549600000039
-37,354.0,5.598160329599997
-38,10.0,5.460549600000039
-39,9.0,5.460549600000039
-40,185.0,5.598160329599997
-41,8.0,5.460549600000039
-42,9.0,5.460549600000039
-43,9.0,5.460549600000039
-44,10.0,5.460549600000039
-45,12.0,5.460549600000039
-46,18.0,5.460549600000039
-47,216.0,5.657124489600028
-48,9.0,5.460549600000039
-49,8.0,5.460549600000039
-50,9.0,5.460549600000039
-51,11.0,5.460549600000039
-52,414.0,5.657124489600028
-53,10.0,5.460549600000039
-54,12.0,5.460549600000039
-55,7.0,5.460549600000039
-56,17.0,5.460549600000039
-57,7.0,5.460549600000039
-58,9.0,5.460549600000039
-59,9.0,5.460549600000039
-60,14.0,5.460549600000039
diff --git a/Ionsmaxes1711440006.csv b/Ionsmaxes1711440006.csv
deleted file mode 100644
index 50fe9ce55b5eaf8c0fff10e1323831b6c18f8ed0..0000000000000000000000000000000000000000
--- a/Ionsmaxes1711440006.csv
+++ /dev/null
@@ -1,27 +0,0 @@
-,Stepcount,IonVal
-0,1.0,0.0029939040000403
-1,1.0,0.0029939040000403
-2,1.0,0.0029939040000403
-3,1.0,0.0029939040000403
-4,1.0,0.0029939040000403
-5,1.0,0.0029939040000403
-6,155.0,5.460549600000039
-7,171.0,5.657124489600028
-8,42.0,3.924638985600001
-9,352.0,5.123131152000033
-10,190.0,5.460549600000039
-11,192.0,5.460549600000039
-12,21.0,5.460549600000039
-13,37.0,5.460549600000039
-14,40.0,5.460549600000039
-15,78.0,5.460549600000039
-16,59.0,5.460549600000039
-17,50.0,5.460549600000039
-18,44.0,5.460549600000039
-19,55.0,5.460549600000039
-20,21.0,5.123131152000033
-21,13.0,5.460549600000039
-22,36.0,5.460549600000039
-23,54.0,5.460549600000039
-24,290.0,5.657124489600028
-25,387.0,5.657124489600028
diff --git a/Ionsmaxes1711440260.csv b/Ionsmaxes1711440260.csv
deleted file mode 100644
index 516fb664c8b7ab72f6b89f175a89574e041f4299..0000000000000000000000000000000000000000
--- a/Ionsmaxes1711440260.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,Stepcount,IonVal
-0,1.0,0.0029939040000403
-1,1.0,0.0029939040000403
-2,1.0,0.0029939040000403
-3,1.0,0.0029939040000403
-4,1.0,0.0029939040000403
-5,29.0,5.460549600000039
-6,24.0,5.460549600000039
-7,23.0,5.460549600000039
-8,209.0,5.657124489600028
-9,60.0,5.657124489600028
-10,22.0,5.460549600000039
-11,104.0,5.657124489600028
diff --git a/Ionsmaxes1711440262.csv b/Ionsmaxes1711440262.csv
deleted file mode 100644
index 516fb664c8b7ab72f6b89f175a89574e041f4299..0000000000000000000000000000000000000000
--- a/Ionsmaxes1711440262.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,Stepcount,IonVal
-0,1.0,0.0029939040000403
-1,1.0,0.0029939040000403
-2,1.0,0.0029939040000403
-3,1.0,0.0029939040000403
-4,1.0,0.0029939040000403
-5,29.0,5.460549600000039
-6,24.0,5.460549600000039
-7,23.0,5.460549600000039
-8,209.0,5.657124489600028
-9,60.0,5.657124489600028
-10,22.0,5.460549600000039
-11,104.0,5.657124489600028
diff --git a/Logs_alg1717210514.pkl b/Logs_alg1717210514.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..714949cfd1273cfd7de6b531c70bbe8d06107208
Binary files /dev/null and b/Logs_alg1717210514.pkl differ
diff --git a/Logs_alg1717262897.pkl b/Logs_alg1717262897.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..be113231d7f3a504ce73f493717016a5571b64f7
Binary files /dev/null and b/Logs_alg1717262897.pkl differ
diff --git a/Logs_env1717262884.pkl b/Logs_env1717262884.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2ffa797c0facc6a957ebf69bebd5167a3e86bb61
Binary files /dev/null and b/Logs_env1717262884.pkl differ
diff --git a/Logs_env1717262892.pkl b/Logs_env1717262892.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2ffa797c0facc6a957ebf69bebd5167a3e86bb61
Binary files /dev/null and b/Logs_env1717262892.pkl differ
diff --git a/ionen_env/envs/Ionenbeschl.py b/ionen_env/envs/Ionenbeschl.py
index 4d39b4ca71aa8973e5864c60adcd348fc3a9b640..86515e4391a2c18209eac0119944c2e40b98619f 100644
--- a/ionen_env/envs/Ionenbeschl.py
+++ b/ionen_env/envs/Ionenbeschl.py
@@ -5,9 +5,10 @@ import os
import numpy as np
import pandas
+import pickle
-import matplotlib
import matplotlib.pyplot as plt
+from collections import defaultdict
import gymnasium as gym
import pandas as pd
@@ -131,11 +132,14 @@ class IonenBeschlEnv(gym.Env):
if data_path is not None:
self.data_path = data_path
self.action_mul=action_mul
- self.counter = 0
- self.ions = []
- self.ionsmax = pd.DataFrame(columns=["Stepcount", "IonVal"])
- self.epimax = (0,-1)
+ # self.counter = 0
+ # self.ions = []
+ # self.ionsmax = pd.DataFrame(columns=["Stepcount", "IonVal"])
+ # self.epimax = (0,-1)
+
+ self.iteration = 0
+ self.logs = defaultdict(list)
low = np.array(
[
@@ -243,21 +247,21 @@ class IonenBeschlEnv(gym.Env):
def set_data_path(self, path: str):
self.data_path = path
- def plot_Ions(self, terminated: bool = False):
- os.makedirs('plots_t', exist_ok=True)
- fig = plt.figure(figsize=(10,10))
- plt.plot(self.ions)
- ts = int(time.time() * 1000)
- if terminated:
- plt.title(f"Ion Values {ts}, step count:{self.counter}, TERMINATED")
- else:
- plt.title(f"Ion Values {ts}, step count:{self.counter}")
- saveloc = f'plots_t/{ts}.png'
- plt.savefig(saveloc)
- plt.close(fig)
-
- def export_ionmaxs(self):
- self.ionsmax.to_csv(f'Ionsmaxes{int(time.time())}.csv')
+ # def plot_Ions(self, terminated: bool = False):
+ # os.makedirs('plots_t', exist_ok=True)
+ # fig = plt.figure(figsize=(10,10))
+ # plt.plot(self.ions)
+ # ts = int(time.time() * 1000)
+ # if terminated:
+ # plt.title(f"Ion Values {ts}, step count:{self.counter}, TERMINATED")
+ # else:
+ # plt.title(f"Ion Values {ts}, step count:{self.counter}")
+ # saveloc = f'plots_t/{ts}.png'
+ # plt.savefig(saveloc)
+ # plt.close(fig)
+
+ # def export_ionmaxs(self):
+ # self.ionsmax.to_csv(f'Ionsmaxes{int(time.time())}.csv')
def reset(
self,
@@ -267,13 +271,14 @@ class IonenBeschlEnv(gym.Env):
) -> tuple[ObsType, dict[str, Any]]:
super().reset(seed=seed)
- if len(self.ions) > 0 and self.epimax[1] > 0:
- self.plot_Ions()
- self.ionsmax.loc[len(self.ionsmax)] = (self.epimax)
+ # if len(self.ions) > 0 and self.epimax[1] > 0:
+ # self.plot_Ions()
+ # self.ionsmax.loc[len(self.ionsmax)] = (self.epimax)
- self.epimax = (0,-1)
- self.counter = 0
- self.ions = []
+ self.iteration += 1
+ # self.epimax = (0,-1)
+ # self.counter = 0
+ # self.ions = []
self.state = np.array(
[
@@ -296,7 +301,7 @@ class IonenBeschlEnv(gym.Env):
def step(
self, action: ActType
) -> tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]:
- self.counter += 1
+ # self.counter += 1
rate = self.action_mul
state = np.array(
[
@@ -317,9 +322,9 @@ class IonenBeschlEnv(gym.Env):
state = np.append(state, getIonVal(state, self.data_path))
# else:
# state = np.append(state, 0)
- self.ions.append(state[10])
- if state[10] > self.epimax[1]:
- self.epimax = (self.counter,state[10])
+ # self.ions.append(state[10])
+ # if state[10] > self.epimax[1]:
+ # self.epimax = (self.counter,state[10])
info = {
"Ion_Value": state[10]
}
@@ -329,10 +334,11 @@ class IonenBeschlEnv(gym.Env):
terminated = self.is_Unhealthy()
if terminated:
reward -=100
- self.plot_Ions(terminated)
- self.ions = []
+ # self.plot_Ions(terminated)
+ # self.ions = []
# Update State
self.state = state
+ self.logs[self.iteration].append([self.state, reward, terminated])
return self.state, reward, terminated, False, info
def get_obs(self):
@@ -350,10 +356,13 @@ class IonenBeschlEnv(gym.Env):
"You are calling render method which is not implemented in this environment. "
)
def close(self):
- self.export_ionmaxs()
+ #self.export_ionmaxs()
+ # self.ionsmax.to_csv(f'Ionsmaxes{int(time.time())}.csv')
+ with open(f'Logs_env{int(time.time())}.pkl', 'wb') as f:
+ pickle.dump(self.logs, f)
+
if self.screen is not None:
import pygame
-
pygame.display.quit()
pygame.quit()
self.isopen = False
\ No newline at end of file
diff --git a/ionen_env/envs/__pycache__/Ionenbeschl.cpython-311.pyc b/ionen_env/envs/__pycache__/Ionenbeschl.cpython-311.pyc
index 86b74422f9029d7a3c4c721898fec3a78e1b8b97..601824366645bbf5c2afff0e4337742883a23aa5 100644
Binary files a/ionen_env/envs/__pycache__/Ionenbeschl.cpython-311.pyc and b/ionen_env/envs/__pycache__/Ionenbeschl.cpython-311.pyc differ
diff --git a/main.py b/main.py
index ac98cf01b02e50f7a36566001049e8fa6c7e76b1..6e907c87ea0bd695b55fc881b29ee29422093743 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,6 @@
import multiprocessing
import os.path
+import pickle
import string
import time
from typing import SupportsFloat, Any
@@ -39,383 +40,13 @@ from torchrl.objectives import ClipPPOLoss
from torchrl.objectives.value import GAE
from tqdm import tqdm
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3.common import results_plotter
-from stable_baselines3.common.logger import configure
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import VecMonitor
-from stable_baselines3.common.results_plotter import load_results, ts2xy, plot_results
-from stable_baselines3.common.callbacks import BaseCallback
import ionen_env
"""
Global Variables:
"""
-MODELPATH = "model.pth"
-class PPOMemory:
- def __init__(self, batch_size):
- self.states = []
- self.probs = []
- self.vals = []
- self.actions = []
- self.rewards = []
- self.dones = []
-
- self.batch_size = batch_size
-
- def generate_batches(self):
- n_states = len(self.states)
- batch_start = np.arange(0, n_states, self.batch_size)
- indices = np.arange(n_states, dtype=np.int64)
- np.random.shuffle(indices)
- batches = [indices[i:i+self.batch_size] for i in batch_start]
-
- return np.array(self.states),\
- np.array(self.actions),\
- np.array(self.probs),\
- np.array(self.vals),\
- np.array(self.rewards),\
- np.array(self.dones),\
- batches
- def store_memory(self, state, action, probs, vals, reward, done):
- self.states.append(state)
- self.probs.append(probs)
- self.vals.append(vals)
- self.rewards.append(reward)
- self.dones.append(done)
- self.actions.append(action)
-
- def clear_memory(self):
- self.states = []
- self.actions = []
- self.probs = []
- self.rewards = []
- self.dones = []
- self.vals = []
-
-class ActorNetwork(nn.Module):
- def __init__(self, n_actions, input_dims, alpha, fc1_dims=256, fc2_dims=256, chkpt_dir='tmp/ppo'):
- super(ActorNetwork, self).__init__()
-
- self.checkpoint_file = os.path.join(chkpt_dir, 'actor_torch_ppo')
- self.actor = nn.Sequential(
- nn.Linear(*input_dims, fc1_dims),
- nn.ReLU(),
- nn.Linear(fc1_dims, fc2_dims),
- nn.ReLU(),
- nn.Linear(fc2_dims, n_actions),
- nn.Softmax(dim=-1)
- )
-
- self.optimizer = optim.Adam(self.parameters(), lr=alpha)
- self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
- self.to(self.device)
-
- def forward(self, state):
- dist = self.actor(state)
- dist = Categorical(dist)
-
- return dist
-
- def save_checkpoint(self):
- torch.save(self.state_dict(), self.checkpoint_file)
-
- def load_checkpoint(self):
- self.load_state_dict(torch.load(self.checkpoint_file))
-
-class CriticNetwork(nn.Module):
- def __init__(self, input_dims, alpha, fc1_dims=256, fc2_dims=256, chkpt_dir='tmp/ppo'):
- super(CriticNetwork, self).__init__()
-
- self.checkpoint_file = os.path.join(chkpt_dir, 'critic_torch_ppo')
- self.critic = nn.Sequential(
- nn.Linear(*input_dims, fc1_dims),
- nn.ReLU(),
- nn.Linear(fc1_dims, fc2_dims),
- nn.ReLU(),
- nn.Linear(fc2_dims, 1)
- )
-
- self.optimizer = optim.Adam(self.parameters(), lr=alpha)
- self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
- self.to(self.device)
-
- def forward(self, state):
- value = self.critic(state)
-
- return value
-
- def save_checkpoint(self):
- torch.save(self.state_dict(), self.checkpoint_file)
-
- def load_checkpoint(self):
- self.load_state_dict(torch.load(self.checkpoint_file))
-
-class Agent:
- def __init__(self, n_actions, input_dims, gamma=0.99, alpha=0.0003, gae_lambda=0.95, policy_clip=0.1, batch_size=64, N=2048, n_epochs=10):
- self.gamma = gamma
- self.policy_clip = policy_clip
- self.n_epochs = n_epochs
- self.gae_lambda = gae_lambda
-
- self.actor = ActorNetwork(n_actions, input_dims, alpha)
- self.critic = CriticNetwork(input_dims, alpha)
- self.memory = PPOMemory(batch_size)
-
- def remember(self, state, action, probs, vals, reward, done):
- self.memory.store_memory(state, action, probs, vals, reward, done)
-
- def save_models(self):
- print('... saving models ...')
- self.actor.save_checkpoint()
- self.critic.save_checkpoint()
-
- def load_models(self):
- print('... loading models ...')
- self.actor.load_checkpoint()
- self.critic.load_checkpoint()
-
- def choose_action(self, observation):
- state = torch.tensor([observation], dtype=torch.float).to(self.actor.device)
-
- dist = self.actor(state)
- value = self.critic(state)
- action = dist.sample()
-
- probs = torch.squeeze(dist.log_prob(action)).item()
- action = torch.squeeze(action).item()
- value = torch.squeeze(value).item()
-
- return action, probs, value
-
- def learn(self):
- for _ in range(self.n_epochs):
- state_arr, action_arr, old_prob_arr, vals_arr, reward_arr, dones_arr, batches = self.memory.generate_batches()
-
- values = vals_arr
- advantage = np.zeros(len(reward_arr), dtype=np.float32)
-
- for t in range(len(reward_arr) - 1):
- discount = 1
- a_t = 0
- for k in range(t, len(reward_arr) - 1):
- a_t += discount * (reward_arr[k] + self.gamma * values[k + 1] * \
- (1 - int(dones_arr[k])) - values[k])
- discount *= self.gamma * self.gae_lambda
- advantage[t] = a_t
- advantage = torch.tensor(advantage).to(self.actor.device)
-
- values = torch.tensor(values).to(self.actor.device)
- for batch in batches:
- states = torch.tensor(state_arr[batch], dtype=torch.float).to(self.actor.device)
- old_probs = torch.tensor(old_prob_arr[batch]).to(self.actor.device)
- actions = torch.tensor(action_arr[batch]).to(self.actor.device)
-
- dist = self.actor(states)
- critic_value = self.critic(states)
-
- critic_value = torch.squeeze(critic_value)
-
- new_probs = dist.log_prob(actions)
- prob_ratio = new_probs.exp() / old_probs.exp()
- # prob_ratio = (new_probs - old_probs).exp()
- weighted_probs = advantage[batch] * prob_ratio
- weighted_clipped_probs = torch.clamp(prob_ratio, 1 - self.policy_clip, 1 + self.policy_clip) * advantage[batch]
- actor_loss = - torch.min(weighted_probs, weighted_clipped_probs).mean()
-
- returns = advantage[batch] + values[batch]
- critic_loss = (returns - critic_value) ** 2
- critic_loss = critic_loss.mean()
-
- total_loss = actor_loss + 0.5 * critic_loss
- self.actor.optimizer.zero_grad()
- self.critic.optimizer.zero_grad()
- total_loss.backward()
- self.actor.optimizer.step()
- self.critic.optimizer.step()
-
- self.memory.clear_memory()
-
-
-"""
-def init_weights(m):
- if isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, mean=0., std=0.1)
- nn.init.constant_(m.bias, 0.1)
-
-class ActorCritic(nn.Module):
- def __init__(self, num_inputs, num_outputs, hidden_size, std=0.0):
- super(ActorCritic, self).__init__()
-
- self.critic = nn.Sequential(
- nn.Linear(num_inputs, hidden_size),
- nn.ReLU(),
- nn.Linear(hidden_size, 1)
- )
-
- self.actor = nn.Sequential(
- nn.Linear(num_inputs, hidden_size),
- nn.ReLU(),
- nn.Linear(hidden_size, num_outputs),
- )
- self.log_std = nn.Parameter(torch.ones(1, num_outputs) * std)
-
- self.apply(init_weights)
-
- def forward(self, x):
- value = self.critic(x)
- mu = self.actor(x)
- std = self.log_std.exp().expand_as(mu)
- dist = Normal(mu, std)
- return dist, value
-
-def plot(frame_idx, rewards):
- plt.figure(figsize=(20, 5))
- plt.subplot(131)
- plt.title('frame %s. reward: %s' % (frame_idx, rewards[-1]))
- plt.plot(rewards)
- plt.show()
-
-def test_env(vis=False):
- state, info = env.reset()
- if vis: env.render()
- done = False
- total_reward = 0
- while not done:
- state = torch.FloatTensor(state).unsqueeze(0).to(device)
- dist, _ = model(state)
- next_state, reward, done, _ = env.step(dist.sample().cpu().numpy()[0])
- state = next_state
- if vis: env.render()
- total_reward += reward
- return total_reward
-
-def compute_gae(next_value, rewards, masks, values, gamma=0.99, tau=0.95):
- values = values + [next_value]
- gae = 0
- returns = []
- for step in reversed(range(len(rewards))):
- delta = rewards[step] + gamma * values[step + 1] * masks[step] - values[step]
- gae = delta + gamma * tau * masks[step] * gae
- returns.insert(0, gae + values[step])
- return returns
-
-def ppo_iter(mini_batch_size, states, actions, log_probs, returns, advantage):
- batch_size = states.size(0)
- for _ in range(batch_size // mini_batch_size):
- rand_ids = np.random.randint(0, batch_size, mini_batch_size)
- yield states[rand_ids, :], actions[rand_ids, :], log_probs[rand_ids, :], returns[rand_ids, :], advantage[rand_ids, :]
-
-def ppo_update(ppo_epochs, mini_batch_size, states, actions, log_probs, returns, advantages, clip_param=0.2):
- for _ in range(ppo_epochs):
- for state, action, old_log_probs, return_, advantage in ppo_iter(mini_batch_size, states, actions, log_probs, returns, advantages):
- dist, value = model(state)
- entropy = dist.entropy().mean()
- new_log_probs = dist.log_prob(action)
-
- ratio = (new_log_probs - old_log_probs).exp()
- surr1 = ratio * advantage
- surr2 = torch.clamp(ratio, 1.0 - clip_param, 1.0 + clip_param) * advantage
-
- actor_loss = - torch.min(surr1, surr2).mean()
- critic_loss = (return_ - value).pow(2).mean()
-
- loss = 0.5 * critic_loss + actor_loss - 0.001 * entropy
-
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
-if __name__ == "__main__":
- use_cuda = torch.cuda.is_available()
- device = torch.device("cuda" if use_cuda else "cpu")
-
- testdata_path = "../Daten/FullInjector_scan_result_2023-09-25_10-20-13-358.csv"
- action_multiplier = 50
-
- env = IonenBeschlEnv(data_path=testdata_path, action_mul=action_multiplier)
- env = TimeLimit(env, max_episode_steps=500)
-
- num_inputs = env.observation_space.shape[0]
- num_outputs = env.action_space.shape[0]
-
- # Hyper params:
- hidden_size = 256
- lr = 3e-4
- num_steps = 20
- mini_batch_size = 5
- ppo_epochs = 4
- threshold_reward = -200
-
- #Uncomment the relevant Line
- #Create a new model for training:
- model = ActorCritic(num_inputs, num_outputs, hidden_size).to(device)
- #Load an existing model for continued use
- # model=torch.load('Ion_ai1.pth')
-
- optimizer = optim.Adam(model.parameters(), lr=lr)
-
- max_frames = 15000
- frame_idx = 0
- test_rewards = []
-
- state, info = env.reset()
- early_stop = False
-
- while frame_idx < max_frames and not early_stop:
-
- log_probs = []
- values = []
- states = []
- actions = []
- rewards = []
- masks = []
- entropy = 0
-
- for _ in range(num_steps):
- state = torch.FloatTensor(state).to(device)
- dist, value = model(state)
-
- action = dist.sample()
- next_state, reward, done, _ = env.step(action.cpu().numpy())
-
- log_prob = dist.log_prob(action)
- entropy += dist.entropy().mean()
-
- log_probs.append(log_prob)
- values.append(value)
- rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
- masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
-
- states.append(state)
- actions.append(action)
-
- state = next_state
- frame_idx += 1
-
- if frame_idx % 1000 == 0:
- test_reward = np.mean([test_env() for _ in range(10)])
- test_rewards.append(test_reward)
- plot(frame_idx, test_rewards)
- if test_reward > threshold_reward: early_stop = True
-
- next_state = torch.FloatTensor(next_state).to(device)
- _, next_value = model(next_state)
- returns = compute_gae(next_value, rewards, masks, values)
-
- returns = torch.cat(returns).detach()
- log_probs = torch.cat(log_probs).detach()
- values = torch.cat(values).detach()
- states = torch.cat(states)
- actions = torch.cat(actions)
- advantage = returns - values
-
- ppo_update(ppo_epochs, mini_batch_size, states, actions, log_probs, returns, advantage)
-
- torch.save(model, 'Ion_ai1.pth')
-""" # Old Unwieldy solution -.-
+MODELPATH = "model1.pth"
# Neuer versuch mit pythorchrl
def torchrlAlg1():
@@ -431,7 +62,7 @@ def torchrlAlg1():
frames_per_batch = 1000
# For a complete training, bring the number of frames up to 1M
- total_frames = 5_000
+ total_frames = 1_000_000
sub_batch_size = 64 # cardinality of the sub-samples gathered from the current data in the inner loop
num_epochs = 10 # optimization steps per batch of data collected
@@ -549,24 +180,21 @@ def torchrlAlg1():
loss_critic_type="smooth_l1",
)
- optim = torch.optim.Adam(loss_module.parameters(), lr)
+ optim = torch.optim.Adam(loss_module.parameters(), lr) # warum adam?
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optim, total_frames // frames_per_batch, 0.0
)
# To load model -> need policy, value and loss module
- print("chkpt1")
- chkpt = torch.load(MODELPATH)
- print("chkpt2")
- policy_module.load_state_dict(chkpt['policy_state_dict'])
- print("chkpt3")
- value_module.load_state_dict(chkpt['value_state_dict'])
- print("chkpt4")
- loss_module.load_state_dict(chkpt['loss_state_dict'])
- print("chkpt5")
-
-
- #TODO: Laden der statedicts
+ # print("chkpt1")
+ # chkpt = torch.load(MODELPATH)
+ # print("chkpt2")
+ # policy_module.load_state_dict(chkpt['policy_state_dict'])
+ # print("chkpt3")
+ # value_module.load_state_dict(chkpt['value_state_dict'])
+ # print("chkpt4")
+ # loss_module.load_state_dict(chkpt['loss_state_dict'])
+ # print("chkpt")
"""
Here begins the Training Loop
@@ -579,15 +207,16 @@ def torchrlAlg1():
Repeat
Repeat
"""
+
logs = defaultdict(list)
pbar = tqdm(total=total_frames)
eval_str = ""
# We iterate over the collector until it reaches the total number of frames it was
# designed to collect:
- ts = int(time.time() * 1000)
- with open(f'plots_t/{ts}start.txt', 'w+') as f:
- f.write('START')
+ # ts = int(time.time() * 1000)
+ # with open(f'plots_t/{ts}start.txt', 'w+') as f:
+ # f.write('START')
for i, tensordict_data in enumerate(collector):
# we now have a batch of data to work with. Let's learn something from it.
@@ -630,12 +259,12 @@ def torchrlAlg1():
# We evaluate the policy once every 10 batches of data.
# Evaluation is rather simple: execute the policy without exploration
# (take the expected value of the action distribution) for a given
- # number of steps (1000, which is our ``env`` horizon).
+ # number of steps (1000, which is our ``env`` horizon). ****500 for me
# The ``rollout`` method of the ``env`` can take a policy as argument:
# it will then execute this policy at each step.
with set_exploration_type(ExplorationType.MEAN), torch.no_grad():
# execute a rollout with the trained policy
- eval_rollout = env.rollout(1000, policy_module)
+ eval_rollout = env.rollout(500, policy_module)
logs["eval reward"].append(eval_rollout["next", "reward"].mean().item())
logs["eval reward (sum)"].append(
eval_rollout["next", "reward"].sum().item()
@@ -653,10 +282,10 @@ def torchrlAlg1():
# this is a nice-to-have but nothing necessary for PPO to work.
scheduler.step()
- # torch.save({'policy_state_dict': policy_module.state_dict(),
- # 'value_state_dict': value_module.state_dict()
- # 'loss_state_dict': loss_module.state_dict()},
- # MODELPATH)
+ torch.save({'policy_state_dict': policy_module.state_dict(),
+ 'value_state_dict': value_module.state_dict(),
+ 'loss_state_dict': loss_module.state_dict()},
+ MODELPATH)
# These modules are not good to load
# 'collector_state_dict': collector.state_dict(),
@@ -691,115 +320,8 @@ def torchrlAlg1():
plt.show()
base_env.close()
# base_env.export_ionmaxs()
-
-
-def sb3Alg():
- is_ipython = 'inline' in matplotlib.get_backend()
-
- np.set_printoptions(suppress=True)
-
- class SaveOnBestTrainingRewardCallback(BaseCallback):
- """
- Callback for saving a model (the check is done every ``check_freq`` steps)
- based on the training reward (in practice, we recommend using ``EvalCallback``).
-
- :param check_freq:
- :param log_dir: Path to the folder where the model will be saved.
- It must contains the file created by the ``Monitor`` wrapper.
- :param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
- """
-
- def __init__(self, check_freq: int, log_dir: str, verbose: int = 1):
- super().__init__(verbose)
- self.check_freq = check_freq
- self.log_dir = log_dir
- self.save_path = os.path.join(log_dir, "best_model")
- self.best_mean_reward = -np.inf
-
- def _init_callback(self) -> None:
- # Create folder if needed
- if self.save_path is not None:
- os.makedirs(self.save_path, exist_ok=True)
-
- def _on_step(self) -> bool:
- value = self.locals.get('new_obs')[0][10]
- self.logger.record('Ion_value', value)
- if self.n_calls % self.check_freq == 0:
- # Retrieve training reward
- x, y = ts2xy(load_results(self.log_dir), "timesteps")
- if len(x) > 0:
- # Mean training reward over the last 100 episodes
- mean_reward = np.mean(y[-100:])
- if self.verbose >= 1:
- print(f"Num timesteps: {self.num_timesteps}")
- print(
- f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}")
-
- # New best model, you could save the agent here
- if mean_reward > self.best_mean_reward:
- self.best_mean_reward = mean_reward
- # Example for saving best model
- if self.verbose >= 1:
- print(f"Saving new best model to {self.save_path}")
- self.model.save(self.save_path)
- return True
-
- # Parallel environments
- log_dir = "logs/"
- os.makedirs(log_dir, exist_ok=True)
-
- new_logger = configure(log_dir, ["stdout", "csv", "tensorboard"])
-
- env = gym.make("Ionen",
- data_path='../Daten/FullInjector_scan_result_2023-09-25_10-20-13-358.csv',
- action_mul=50)
-
- # vec_env = make_vec_env("CartPole-v1", n_envs=1)
- # Create and wrap the environment
- env = Monitor(env, log_dir, info_keywords=("Ion_Value",))
- # vec_env = VecMonitor(vec_env, log_dir)
-
- model = PPO("MlpPolicy", env, tensorboard_log=log_dir, verbose=1)
- callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
- model.set_logger(new_logger)
- timesteps=5_000
- model.learn(total_timesteps=timesteps, callback=callback, tb_log_name="first_run")
- model.save("Ionen")
- plot_results([log_dir], timesteps, results_plotter.X_TIMESTEPS, "Ionen")
- plt.show()
-
- model.env.env_method("export_ionmaxs")
-
- # del model
-
- # model = PPO.load("Ionen")
-
- # model.load("tmp/best_model")
-
- # TODO: Manueles Lernen! => Nicht möglich ohne .learn funktion.??
- # etwas zum git testen
-
-
- # episode_reward = 0
- # obs, info = env.reset()
- # infos = []
- # while True:
- # action, _states = model.predict(obs)
- # obs, rewards, done, truncated, info = env.step(action)
- # # print("Action:", action)
- # print("Observation:", obs)
- # # print("Rewards:", rewards)
- # # print(info)
- # infos.append([info, obs])
- # # env.render('human')
- # episode_reward += rewards
- # if done:
- # # print("Observation:",obs)
- # # print(info)
- # # print("Reward:", episode_reward)
- # # episode_reward = 0.0
- # # obs, info = env.reset()
- # break
+ with open(f'Logs_alg{int(time.time())}.pkl', 'wb') as f:
+ pickle.dump(logs, f)
if __name__ == "__main__":
diff --git a/model.pth b/model.pth
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deleted file mode 100644
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@@ -1 +0,0 @@
-START
\ No newline at end of file
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