fixes to runner
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ff9e971c97
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@ -137,8 +137,9 @@ class OnPolicyRunner:
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if it % self.save_interval == 0:
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self.save(os.path.join(self.log_dir, 'model_{}.pt'.format(it)))
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ep_infos.clear()
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self.current_learning_iteration = num_learning_iterations
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self.save(os.path.join(self.log_dir, 'model_{}.pt'.format(num_learning_iterations)))
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self.current_learning_iteration += num_learning_iterations
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self.save(os.path.join(self.log_dir, 'model_{}.pt'.format(self.current_learning_iteration)))
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def log(self, locs, width=80, pad=35):
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self.tot_timesteps += self.num_steps_per_env * self.env.num_envs
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@ -175,7 +176,7 @@ class OnPolicyRunner:
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self.writer.add_scalar('Train/mean_reward/time', statistics.mean(locs['rewbuffer']), self.tot_time)
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self.writer.add_scalar('Train/mean_episode_length/time', statistics.mean(locs['lenbuffer']), self.tot_time)
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str = f" \033[1m Learning iteration {locs['it']}/{locs['num_learning_iterations']} \033[0m "
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str = f" \033[1m Learning iteration {locs['it']}/{self.current_learning_iteration + locs['num_learning_iterations']} \033[0m "
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if len(locs['rewbuffer']) > 0:
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log_string = (f"""{'#' * width}\n"""
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