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This also adds several smaller features: - torch/examples/watch_atari.py: use a trained agent to play atari. - Error handling in the snapshotter for invalid arguments. - torch/examples/dqn_atari.py: train on atari environments.
This also adds several smaller features: - torch/examples/watch_atari.py: use a trained agent to play atari. - Error handling in the snapshotter for invalid arguments. - torch/examples/dqn_atari.py: train on atari environments.
Codecov Report
@@ Coverage Diff @@
## master #2152 +/- ##
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- Coverage 91.48% 68.00% -23.48%
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Files 195 198 +3
Lines 10675 11037 +362
Branches 1340 1403 +63
===========================================
- Hits 9766 7506 -2260
- Misses 657 3147 +2490
- Partials 252 384 +132
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krzentner
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Nov 9, 2020
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Note this builds on top of the dueling and double PRs so the diff will be redundant. Also will add tests shortly.