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Upgrade DQN to use .log #404

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merged 4 commits into from
Nov 26, 2020
Merged

Upgrade DQN to use .log #404

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7e95325
Upgrade DQN to use .log
teddykoker Nov 24, 2020
175b7cd
remove unused
teddykoker Nov 24, 2020
b334c49
pep8
teddykoker Nov 24, 2020
8174530
fixed other dqn
teddykoker Nov 24, 2020
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14 changes: 2 additions & 12 deletions pl_bolts/models/rl/double_dqn_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,27 +65,17 @@ def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], _) -> OrderedD
if self.global_step % self.sync_rate == 0:
self.target_net.load_state_dict(self.net.state_dict())

log = {
self.log_dict({
"total_reward": self.total_rewards[-1],
"avg_reward": self.avg_rewards,
"train_loss": loss,
# "episodes": self.total_episode_steps,
}
status = {
"steps": self.global_step,
"avg_reward": self.avg_rewards,
"total_reward": self.total_rewards[-1],
"episodes": self.done_episodes,
# "episode_steps": self.episode_steps,
"epsilon": self.agent.epsilon,
}
})

return OrderedDict(
{
"loss": loss,
"avg_reward": self.avg_rewards,
"log": log,
"progress_bar": status,
}
)

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18 changes: 4 additions & 14 deletions pl_bolts/models/rl/dqn_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -288,28 +288,18 @@ def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], _) -> OrderedD
if self.global_step % self.sync_rate == 0:
self.target_net.load_state_dict(self.net.state_dict())

log = {
self.log_dict({
"total_reward": self.total_rewards[-1],
"avg_reward": self.avg_rewards,
"train_loss": loss,
"episodes": self.done_episodes,
"episode_steps": self.total_episode_steps[-1]
}
status = {
"steps": self.global_step,
"avg_reward": self.avg_rewards,
"total_reward": self.total_rewards[-1],
"episodes": self.done_episodes,
"episode_steps": self.total_episode_steps[-1],
"epsilon": self.agent.epsilon,
}
})

return OrderedDict(
{
"loss": loss,
"avg_reward": self.avg_rewards,
"log": log,
"progress_bar": status,
}
)

Expand All @@ -323,8 +313,8 @@ def test_epoch_end(self, outputs) -> Dict[str, torch.Tensor]:
"""Log the avg of the test results"""
rewards = [x["test_reward"] for x in outputs]
avg_reward = sum(rewards) / len(rewards)
tensorboard_logs = {"avg_test_reward": avg_reward}
return {"avg_test_reward": avg_reward, "log": tensorboard_logs}
self.log("avg_test_reward", avg_reward)
return {"avg_test_reward": avg_reward}

def configure_optimizers(self) -> List[Optimizer]:
""" Initialize Adam optimizer"""
Expand Down
14 changes: 2 additions & 12 deletions pl_bolts/models/rl/per_dqn_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,27 +130,17 @@ def training_step(self, batch, _) -> OrderedDict:
if self.global_step % self.sync_rate == 0:
self.target_net.load_state_dict(self.net.state_dict())

log = {
self.log_dict({
"total_reward": self.total_rewards[-1],
"avg_reward": self.avg_rewards,
"train_loss": loss,
# "episodes": self.total_episode_steps,
}
status = {
"steps": self.global_step,
"avg_reward": self.avg_rewards,
"total_reward": self.total_rewards[-1],
"episodes": self.done_episodes,
# "episode_steps": self.episode_steps,
"epsilon": self.agent.epsilon,
}
})

return OrderedDict(
{
"loss": loss,
"avg_reward": self.avg_rewards,
"log": log,
"progress_bar": status,
}
)

Expand Down