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---
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
metrics:
- type: mean_reward
value: '-120.10 +/- 19.30'
name: mean_reward
verified: false
license: afl-3.0
---
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**.
We train a three-layer MLP as the Q-network.
We store the transitions in a replay buffer.
After the network converges, we stop training and validate its performance in
comparison to a random baseline.
Parameters:
```python
hidden_size = 64
gamma = 0.99
epsilon_decay = 0.999
buffer_size = 10000
batch_size = 64
episodes = 10000
``` |