
# **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
```
{"license": "afl-3.0", "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}]}]}]}
MountainCar-v0 deep-reinforcement-learning reinforcement-learning license:afl-3.0 model-index region:us
TAGS
#MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #license-afl-3.0 #model-index #region-us 


# 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:

# 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:
TAGS
#MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #license-afl-3.0 #model-index #region-us 
 # 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:
TAGS
#MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #license-afl-3.0 #model-index #region-us 
# 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: