while not done:
log(test_env, iteration, step_idx, total_rew)
p, _ = network.step(np.array([state]))
# print(p)
action = np.argmax(p)
state, reward, done, _ = test_env.step(action)
step_idx += 1
total_rew += reward
log(test_env, iteration, step_idx, total_rew)
value_losses = []
policy_losses = []
for i in range(1000):
if i % 50 == 0:
test_agent(i)
plt.plot(value_losses, label="value loss")
plt.plot(policy_losses, label="policy loss")
plt.legend()
plt.show()
obs, pis, returns, total_reward, done_state = execute_episode(
network, 32, HillClimbingEnv)
mem.add_all({"ob": obs, "pi": pis, "return": returns})
batch = mem.get_minibatch()
vl, pl = trainer.train(batch["ob"], batch["pi"], batch["return"])
value_losses.append(vl)
policy_losses.append(pl)
