From 0e7048b89f93cd466dc52df4479e7488949daec1 Mon Sep 17 00:00:00 2001 From: pzhokhov Date: Wed, 19 Sep 2018 15:04:54 -0700 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d050b59..a9b7bf6 100644 --- a/README.md +++ b/README.md @@ -87,7 +87,7 @@ 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](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. +docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) 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: