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---
tags:
- SuperMarioBros-v0
- RND
- CNN
- reinforcement-learning
- custom-implementation
- atari
- SuperMarioBros-v0
model-index:
- name: RND-SuperMarioBros-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SuperMarioBros-v0
type: SuperMarioBros-v0
metrics:
- type: mean_reward
value: 2502.70 +/- 418.86
name: mean_reward
verified: false
---
# **RND with CNN** Agent playing **SuperMarioBros-v0**
This is a trained model of a **RND-CNN** agent playing **SuperMarioBros-v0** .
To learn to use this model and train yours check this notebook on kaggle: https://www.kaggle.com/code/syedjarullahhisham/drl-extra-personal-unit-5-rnd-super-mario-bros
## Codes
Github repos(Give a star if found useful):
* https://github.com/hishamcse/DRL-Renegades-Game-Bots
* https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
* https://github.com/hishamcse/Robo-Chess
Kaggle Notebook:
* https://www.kaggle.com/code/syedjarullahhisham/drl-extra-personal-unit-5-rnd-super-mario-bros
* https://www.kaggle.com/code/syedjarullahhisham/drl-extra-personal-unit-5-rnd-montezuma-mario-bros
# HyperParameters:
```
"trainmethod": "RND",
"envid": "SuperMarioBros-v0",
"maxstepperepisode": 18000,
"learningrate": 0.0001,
"numenv": 128,
"numstep": 128,
"gamma": 0.999,
"intgamma": 0.99,
"lambda": 0.95,
"usegae": true,
"clipgradnorm": 0.5,
"entropy": 0.001,
"epoch": 4,
"minibatch": 4,
"ppoeps": 0.1,
"extcoef": 5.0,
"intcoef": 1.0,
"stickyaction": true,
"actionprob": 0.25,
"lifedone": false,
"obsnormstep": 50
``` |