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.ipynb_checkpoints

.ipynb_checkpoints

 
 

Actor_critic

Actor_critic

 
 

DDPG

DDPG

 
 

DQNs+Variants

DQNs+Variants

 
 

Meta-RL/MAML

Meta-RL/MAML

 
 

Policy_grad

Policy_grad

 
 

RL_Coursera_Specialisation

RL_Coursera_Specialisation

 
 

RL_project1nav

RL_project1nav

 
 

__pycache__

__pycache__

 
 

adaptive_noise_scaling

adaptive_noise_scaling

 
 

pygame

pygame

 
 

README.md

README.md

 
 

RL_taxi_task.ipynb

RL_taxi_task.ipynb

 
 

intro_to_gym_environment.ipynb

intro_to_gym_environment.ipynb

 
 

pytorch_inference_validation.ipynb

pytorch_inference_validation.ipynb

 
 

pytorch_transfer_learning.ipynb

pytorch_transfer_learning.ipynb

 
 

test_for_doubts.ipynb

test_for_doubts.ipynb

 
 

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Things implemented ::

  1. Normal taxi-gym environment and solved with Q-learning
  2. Vanilla DQNs
  3. DQNS with variants
    • Double DQNs
    • Dueling DQNs
  4. Policy based models
    • Adaptive Noise Scaling
    • Hill Climbing

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Programs on concepts that I have learnt in RL.

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