fixes DQN run_n_episodes using the wrong environment variable #525
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sidhantls
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akihironitta,
ananyahjha93 and
Borda
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## master #525 +/- ##
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Coverage 78.95% 78.95%
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Files 105 105
Lines 6121 6121
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Hits 4833 4833
Misses 1288 1288
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| @@ -171,7 +171,7 @@ def run_n_episodes(self, env, n_epsiodes: int = 1, epsilon: float = 1.0) -> List | |||
| while not done: | |||
| self.agent.epsilon = epsilon | |||
| action = self.agent(episode_state, self.device) | |||
| next_state, reward, done, _ = self.env.step(action[0]) | |||
| next_state, reward, done, _ = env.step(action[0]) | |||
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shall we also assign the env back, self.env = env?
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if we assign the test env to self.env in test_step, when training is done after testing, it'll use the test env (without seed) instead of what it was initialized with for training (env with seed)
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@sid-sundrani it seems that the macOS test is hanging, mind have look at it? |
happening on 3.8. sure I'm looking into it |
it seems that it is hanging on master so most likely unrelated to this PR, but very welcome to find the hanging cases... |
yes, likely unrelated as the the hanging cases are unusual- in this PR, the pytest gets canceled after test_simclr, 3 more tests later than where the same pytest was canceled in the previous pr that was merged. i remember there was an issue in the past with perhaps OOM in a self_supervised failing test (#409) |
What does this PR do?
DQN's
run_n_episodesmethod, which is used bytest_stepfor testing the agent, uses the object's environmentself.envand not its argumentenvto run the simulation steps. It resetsenvbut takes simulation steps inself.env.As a result, the testing stats are wrong because the wrong environment is used by the agent and a different environment is being reset after each episode
Fixes #516
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