diff --git a/README.md b/README.md
index 6ac8580..d050b59 100644
--- a/README.md
+++ b/README.md
@@ -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: