From e09a4dd03f942448d92851c0d2f5578b7a8194cc Mon Sep 17 00:00:00 2001
From: Armin <armin.co@hs-bochum.de>
Date: Mon, 4 Jan 2021 11:44:06 +0100
Subject: [PATCH] Refactoring
---
.gitignore | 3 ++-
environment_wrapper.py | 19 +++++++++++--------
main.py | 20 +++++++++++---------
3 files changed, 24 insertions(+), 18 deletions(-)
diff --git a/.gitignore b/.gitignore
index b9518e5..43f67a2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
__pycache__
-saved_agents
\ No newline at end of file
+saved_agents
+*.png
\ No newline at end of file
diff --git a/environment_wrapper.py b/environment_wrapper.py
index a3945f1..a9e1920 100644
--- a/environment_wrapper.py
+++ b/environment_wrapper.py
@@ -24,18 +24,18 @@ def one_episode(environment, agent, render, learn, max_steps=3000):
score = 0
state = reset(environment)
for _ in range(max_steps):
- action = agent.get_action(state)
if render:
environment.render()
+ action = agent.get_action(state)
following_state, reward, done, _ = step(environment, action)
agent.remember(state, action, reward, following_state, done)
score += reward
state = following_state
if learn:
- loss = agent.learn()
+ agent.learn()
if done:
- if learn:
- print(loss)
+ if learn:
+ agent.learn(offline=True)
break
return score
@@ -60,16 +60,19 @@ def run(environment, agent, episodes, render=True, learn=True):
score_history = []
avg_score_history = []
- for _ in tqdm(range(episodes), desc="Training", unit="Episode"):
+ pbar = trange(episodes, desc='Score [actual, average]: [0, 0]', unit="Episodes")
+ for _ in pbar:
score = one_episode(environment, agent, render, learn)
score_history.append(score)
- is_solved = np.mean(score_history[-100:])
+ is_solved = np.mean(score_history[-50:])
avg_score_history.append(is_solved)
- if is_solved > 235 and learn:
+ if is_solved > 200 and learn:
break
- print("Score [actual, average]: [{0:.2f}, {1:.2f}]\n".format(score, is_solved))
+ desc = ("Score [actual, average]: [{0:.2f}, {1:.2f}]".format(score, is_solved))
+ pbar.set_description(desc)
+ pbar.refresh()
return score_history, avg_score_history
diff --git a/main.py b/main.py
index 704a05a..daea0e6 100644
--- a/main.py
+++ b/main.py
@@ -6,7 +6,7 @@ import os
import atexit
import gym
-from agents import DQAgent
+from agents import QAgent
import environment_wrapper as ew
# Allow GPU usage or force tensorflow to use the CPU.
@@ -21,18 +21,20 @@ if __name__ == '__main__':
env = gym.make('LunarLander-v2')
# 2. Create a learning agent
- marvin = DQAgent(env.action_space.n, env.observation_space.shape[0], 'double_test')
+ marvin = QAgent(env.action_space.n, env.observation_space.shape[0], 'from_scratch')
# (2.5) *optional* Load agent memory and/or net from disk.
- marvin.load('saved_agents/large_memory/large_memory', net=False)
+ LOAD_MEMORIES = False
+ LOAD_ANN = False
+ marvin.load('saved_agents/agent/agent', net=LOAD_ANN, memory=LOAD_MEMORIES)
# 3. Set your configurations for the run.
- RENDER = True
+ RENDER = False
LEARNING = True
- LEARN_ONLINE = False
- LEARN_OFFLINE = True
- RUN_EPISODES = 200
- LEARN_OFFLINE_EPOCHS = 100
+ LEARN_ONLINE = True
+ LEARN_OFFLINE = False
+ RUN_EPISODES = 500
+ LEARN_OFFLINE_EPOCHS = 500
SAVE_PATH = "./saved_agents"
# Register an *atexit* callback,
@@ -59,4 +61,4 @@ if __name__ == '__main__':
# Show the result of the runl.
if RENDER:
- ew.process_logs(avg_score, loss)
+ ew.process_logs(avg_score, loss, title=marvin.name)
--
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