GaussianLSTMPolicy with model #677
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## master #677 +/- ##
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+ Coverage 64.08% 64.62% +0.53%
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Files 159 161 +2
Lines 9770 9924 +154
Branches 1293 1303 +10
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+ Hits 6261 6413 +152
- Misses 3182 3183 +1
- Partials 327 328 +1
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ryanjulian
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| """GaussianLSTMPolicy with GaussianLSTMModel.""" | |||
| from akro.tf import Box | |||
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| Returns: | ||
| action (numpy.ndarray): Predicted action. | ||
| agent_info (dict[numpy.ndarray]): Mean and log std of the |
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maybe be explicit about the contents of the dict, e.g.
https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/batch_polopt.py#L85
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| Returns: | ||
| actions (numpy.ndarray): Predicted actions. | ||
| agent_infos (dict[numpy.ndarray]): Mean and log std of the |
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be explicit about keys in the dict https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/batch_polopt.py#L85
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| Returns: | ||
| action (numpy.ndarray): Predicted action. | ||
| agent_info (dict[numpy.ndarray]): Mean and log std of the |
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Can you please make it multi layer recurrent? Single layer sometimes does not work well for hard problem.. |
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@zhanpenghe I think another PR will be more appropriate. |
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File an issue then. |
This PR refactored GaussianLSTMPolicy with garage.tf.models.Model. It added two classes: GaussianLSTMModel and GaussianLSTMPolicyWithModel.
Added GaussianLSTMModel and GaussianLSTMPolicyWithModel.
Added test for PPO with GaussianLSTMPolicyWithModel.
Apart from testing functionality of GaussianLSTMPolicyWithModel
in test_gaussian_lstm_policy_with_model.py, transitions from the
old policy (GaussianLSTMPolicy) to the new policy
(GaussianLSTMPolicyWithModel) are also tested in
test_gaussian_lstm_policy_with_model_transit.py, to make sure
they have the same API.