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Modernization #5

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Modernization #5

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10 changes: 5 additions & 5 deletions cartpole.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ def __init__(self, observation_space, action_space):
self.model.add(Dense(24, input_shape=(observation_space,), activation="relu"))
self.model.add(Dense(24, activation="relu"))
self.model.add(Dense(self.action_space, activation="linear"))
self.model.compile(loss="mse", optimizer=Adam(lr=LEARNING_RATE))
self.model.compile(loss="mse", optimizer=Adam(learning_rate=LEARNING_RATE))

def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
Expand All @@ -61,28 +61,28 @@ def experience_replay(self):


def cartpole():
env = gym.make(ENV_NAME)
env = gym.make(ENV_NAME) #render_mode='human'
score_logger = ScoreLogger(ENV_NAME)
observation_space = env.observation_space.shape[0]
action_space = env.action_space.n
dqn_solver = DQNSolver(observation_space, action_space)
run = 0
while True:
run += 1
state = env.reset()
state, info = env.reset()
state = np.reshape(state, [1, observation_space])
step = 0
while True:
step += 1
#env.render()
action = dqn_solver.act(state)
state_next, reward, terminal, info = env.step(action)
state_next, reward, terminal, truncated, info = env.step(action)
reward = reward if not terminal else -reward
state_next = np.reshape(state_next, [1, observation_space])
dqn_solver.remember(state, action, reward, state_next, terminal)
state = state_next
if terminal:
print "Run: " + str(run) + ", exploration: " + str(dqn_solver.exploration_rate) + ", score: " + str(step)
print( "Run: " + str(run) + ", exploration: " + str(dqn_solver.exploration_rate) + ", score: " + str(step))
score_logger.add_score(step, run)
break
dqn_solver.experience_replay()
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14 changes: 8 additions & 6 deletions scores/score_logger.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,10 +38,10 @@ def add_score(self, score, run):
show_legend=True)
self.scores.append(score)
mean_score = mean(self.scores)
print "Scores: (min: " + str(min(self.scores)) + ", avg: " + str(mean_score) + ", max: " + str(max(self.scores)) + ")\n"
print( "Scores: (min: " + str(min(self.scores)) + ", avg: " + str(mean_score) + ", max: " + str(max(self.scores)) + ")\n" )
if mean_score >= AVERAGE_SCORE_TO_SOLVE and len(self.scores) >= CONSECUTIVE_RUNS_TO_SOLVE:
solve_score = run-CONSECUTIVE_RUNS_TO_SOLVE
print "Solved in " + str(solve_score) + " runs, " + str(run) + " total runs."
print( "Solved in " + str(solve_score) + " runs, " + str(run) + " total runs." )
self._save_csv(SOLVED_CSV_PATH, solve_score)
self._save_png(input_path=SOLVED_CSV_PATH,
output_path=SOLVED_PNG_PATH,
Expand All @@ -58,10 +58,12 @@ def _save_png(self, input_path, output_path, x_label, y_label, average_of_n_last
y = []
with open(input_path, "r") as scores:
reader = csv.reader(scores)
data = list(reader)
for i in range(0, len(data)):
x.append(int(i))
y.append(int(data[i][0]))
i = 0
for row in reader:
if row:
x.append(int(i))
y.append(int(row[0]))
i += 1

plt.subplots()
plt.plot(x, y, label="score per run")
Expand Down