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CartPole_Sarsa_QLearning.py
CartPole_Sarsa_QLearning.py
 
 
CartPole_Sarsa_QLearning_PolynomialValueFunction.py
CartPole_Sarsa_QLearning_PolynomialValueFunction.py
 
 
Cartpole.py
Cartpole.py
 
 
Cartpole_fchc.py
Cartpole_fchc.py
 
 
GridWorld_CartPole_TemporalDifferenceLearning.py
GridWorld_CartPole_TemporalDifferenceLearning.py
 
 
GridWorld_OptimalPolicy.py
GridWorld_OptimalPolicy.py
 
 
GridWorld_RandomPolicy.py
GridWorld_RandomPolicy.py
 
 
GridWorld_Sara_QLearning.py
GridWorld_Sara_QLearning.py
 
 
GridWorld_SarsaLambda_QLambda_ActorCritic_REINFORCE.py
GridWorld_SarsaLambda_QLambda_ActorCritic_REINFORCE.py
 
 
GridWorld_SoftmaxActionSelection.py
GridWorld_SoftmaxActionSelection.py
 
 
GridWorld_fchc_ce.py
GridWorld_fchc_ce.py
 
 
MountainCar.py
MountainCar.py
 
 
MountainCar_SarsaLambda_QLambda_ActorCritic_REINFORCE.py
MountainCar_SarsaLambda_QLambda_ActorCritic_REINFORCE.py
 
 
README.md
README.md
 
 

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Reinforcement_Learning

Implementation of RL algorithms in various environments

Environments Coded:

  1. GridWorld Environment
  2. CartPole
  3. Mountain Car

Algorithms Coded:

  1. Cross Entropy
  2. First Choice Hill Climbing
  3. Temporal Difference Learning
  4. Sarsa Learning
  5. Q Learning
  6. TD-Lambda
  7. Sarsa-Lambda
  8. Q-Lambda
  9. Actor Critic
  10. REINFORCE

Action Selection Techniques:

  1. Epsilon Greedy
  2. Softmax

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Implementation of RL algorithms in various environments

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reinforcement-learning gridworld-environment mountaincar cartpole-environment

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