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2018-09-19 15:04:01 -07:00
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@@ -86,8 +86,8 @@ python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timeste
```
will set entropy coeffient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same)
See docstrings in [common/models.py](common/models.py) for description of network parameters for each type of model, and
docstring for [baselines/ppo2/ppo2.py/learn()](ppo2/ppo2.py) for the description of the ppo2 hyperparamters.
See docstrings in [common/models.py](baselines/common/models.py) for description of network parameters for each type of model, and
docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py) for the description of the ppo2 hyperparamters.
### Example 2. DQN on Atari
DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong: