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Fixed typo on #092 (#824)

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Darío Hereñú
2019-04-01 19:41:52 -03:00
committed by pzhokhov
parent 58541db226
commit b1644157d6
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README.md
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@@ -89,7 +89,7 @@ python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timeste
will set entropy coefficient 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) will set entropy coefficient 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](baselines/common/models.py) for description of network parameters for each type of model, and 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#L152) for the description of the ppo2 hyperparamters. docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparameters.
### Example 2. DQN on Atari ### 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: DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong: