This project solves the famous Frozen Lake environment using a reinforcement learning (RL) method known as cross-entropy. Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice. See my blog post for more details.
Blog post: Using the cross-entropy method to solve Frozen Lake on DEV
git clone https://github.com/exactful/cross-entropy-rl.git
cd cross-entropy-rl
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt
python3 cross-entropy-rl-train.py
- Create a neural network
- Use the neural network to play a batch of episodes using the states as inputs and the outputs as actions
- Set a reward threshold and filter out "bad" episodes that have episode rewards below the threshold
- Train the neural network on the remaining "good" episodes using the states as inputs and the actions taken as desired outputs; our objective will be to minimise the loss between the neural network output and the desired output
- Repeat from step 2 until the mean episode reward of a batch is good enough
Running the code leads to a solution being found after around 120 episodes:
0: Loss: 1.3657 Mean ep reward: 0.01 Mean disc ep reward: 0.0048
1: Loss: 1.3814 Mean ep reward: 0.03 Mean disc ep reward: 0.0088
2: Loss: 1.3742 Mean ep reward: 0.0 Mean disc ep reward: 0.0
3: Loss: 1.3726 Mean ep reward: 0.02 Mean disc ep reward: 0.0055
4: Loss: 1.3654 Mean ep reward: 0.01 Mean disc ep reward: 0.0035
5: Loss: 1.3597 Mean ep reward: 0.03 Mean disc ep reward: 0.0078
6: Loss: 1.3542 Mean ep reward: 0.02 Mean disc ep reward: 0.0042
7: Loss: 1.3504 Mean ep reward: 0.0 Mean disc ep reward: 0.0
8: Loss: 1.3466 Mean ep reward: 0.0 Mean disc ep reward: 0.0
9: Loss: 1.3447 Mean ep reward: 0.01 Mean disc ep reward: 0.0021
10: Loss: 1.3438 Mean ep reward: 0.04 Mean disc ep reward: 0.0116
11: Loss: 1.3393 Mean ep reward: 0.04 Mean disc ep reward: 0.0122
12: Loss: 1.3352 Mean ep reward: 0.01 Mean disc ep reward: 0.0028
13: Loss: 1.3291 Mean ep reward: 0.01 Mean disc ep reward: 0.0023
14: Loss: 1.323 Mean ep reward: 0.04 Mean disc ep reward: 0.011
...
...
...
111: Loss: 0.5678 Mean ep reward: 0.62 Mean disc ep reward: 0.3
112: Loss: 0.4479 Mean ep reward: 0.67 Mean disc ep reward: 0.3268
113: Loss: 0.3356 Mean ep reward: 0.6 Mean disc ep reward: 0.2909
114: Loss: 0.3315 Mean ep reward: 0.7 Mean disc ep reward: 0.3418
116: Loss: 0.4725 Mean ep reward: 0.64 Mean disc ep reward: 0.3193
117: Loss: 0.3119 Mean ep reward: 0.77 Mean disc ep reward: 0.3836
118: Loss: 0.3133 Mean ep reward: 0.67 Mean disc ep reward: 0.3338
120: Loss: 0.4974 Mean ep reward: 0.71 Mean disc ep reward: 0.3516
121: Loss: 0.3878 Mean ep reward: 0.68 Mean disc ep reward: 0.3379
122: Loss: 0.3064 Mean ep reward: 0.72 Mean disc ep reward: 0.3603
124: Loss: 0.4871 Mean ep reward: 0.7 Mean disc ep reward: 0.3469
125: Loss: 0.3627 Mean ep reward: 0.7 Mean disc ep reward: 0.3479
126: Loss: 0.2901 Mean ep reward: 0.76 Mean disc ep reward: 0.3791
128: Loss: 0.4612 Mean ep reward: 0.71 Mean disc ep reward: 0.3554
129: Loss: 0.2774 Mean ep reward: 0.79 Mean disc ep reward: 0.3982
Environment solved!
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