haoshengzou
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983cd36074
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finished all ppo examples. Training is remarkably slower than the version before Jan 13. More strangely, in the gym example there's almost no improvement... but this problem comes behind design. I'll first write actor-critic.
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2018-01-15 00:03:06 +08:00 |
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haoshengzou
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fed3bf2a12
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auto target network. ppo_cartpole.py run ok. but results is different from previous version even with the same random seed, still needs debugging.
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2018-01-14 20:58:28 +08:00 |
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haoshengzou
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dfcea74fcf
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fix memory growth and slowness caused by sess.run(tf.multinomial()), now ppo examples are working OK with slight memory growth (1M/min), which still needs research
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2018-01-03 20:32:05 +08:00 |
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haoshengzou
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4333ee5d39
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ppo_cartpole.py seems to be working with param: bs128, num_ep20, max_time500; manually merged Normal from branch policy_wrapper
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2018-01-02 19:40:37 +08:00 |
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haoshengzou
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b33a141373
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towards policy/value refactor
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2017-12-23 17:25:16 +08:00 |
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Haosheng Zou
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7ab211b63c
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preliminary design of dqn_example, dqn interface. identify the assign of networks
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2017-12-13 20:47:45 +08:00 |
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rtz19970824
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0c4a83f3eb
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vanilla policy gradient
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2017-12-11 13:37:27 +08:00 |
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haosheng
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a00b930c2c
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fix naming and comments of coding style, delete .json
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2017-12-10 17:23:13 +08:00 |
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haosheng
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ff4306ddb9
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model-free rl first commit, with ppo_example.py in examples/ and task delegations in ppo_example.py and READMEs
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2017-12-08 21:09:23 +08:00 |
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