add policy saving
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@@ -93,6 +93,11 @@ def training_function(config):
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disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'],
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disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'],
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mean_episode_length=info['gen/mean_episode_length'])
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mean_episode_length=info['gen/mean_episode_length'])
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# save model checkpoints
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ep = info['epoch'] + 1
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if (ep % 25 == 0):
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torch.save(info['policy'].state_dict(), f'policy_epoch{ep}.pt')
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value, policy = gail_ppo(
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value, policy = gail_ppo(
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env_fn=env_fn,
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env_fn=env_fn,
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expert_data=expert_data,
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expert_data=expert_data,
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@@ -116,7 +121,8 @@ def training_function(config):
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lr_schedulers=[pi_lr_scheduler],
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lr_schedulers=[pi_lr_scheduler],
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)
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)
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# save value, policy
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# save model
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torch.save(policy.state_dict(), 'policy_final.pt')
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analysis = tune.run(
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analysis = tune.run(
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