batch_size=4,
n_actions=n_actions,
eps_end=0.01,
input_dims=[input_dims],
lr=0.003)
scores, avg_scores, eps_history = [], [], []
epochs = 500
for epoch in range(epochs):
score = 0
done = False
state_old = env.reset()
# print(state_old[0].type)
while not done: # iterating over every timestep (state)
env.render()
action = agent.choose_action(state_old)
state_new, reward, done, info = env.step(action)
score += reward
agent.store_transition(state_old, action, reward, state_new, done)
agent.learn()
state_old = state_new
scores.append(score)
eps_history.append(agent.epsilon)
avg_score = np.mean(scores[-100:])
avg_scores.append(avg_score)
print("epoch: ", epoch, "score: %.2f " % score,
"avg_score: %.2f " % avg_score, "epsilon: %.2f" % agent.epsilon)
simple_plot(scores, avg_scores, epoch)
epsilon=1.0,
batch_size=64,
n_actions=4,
eps_end=0.01,
input_dims=[8],
lr=0.001)
scores, eps_history = [], []
n_games = 500
for i in range(n_games):
score = 0
done = False
observation = env.reset()
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.store_transition(observation, action, reward, observation_,
done)
agent.learn()
observation = observation_
#env.render()
scores.append(score)
eps_history.append(agent.epsilon)
avg_score = np.mean(scores[-100:])
print('episode ', i, 'score %.2f' % score,
'average score %.2f' % avg_score, 'epsilon %.2f' % agent.epsilon)
