GitHub - harmanpreet93/reinforcement-learning: Reinforcement Learning algorithms

Reinforcement Learning - COMP-767

RL algorithms and concepts implementation:

Policy Gradient Methods
- REINFORCE algorithm with MLP
- Actor-Critic Method
Semi-Gradient TD Lamda with Eligibility Traces
Function Approximation: Linear and Non-Linear (Deep Neural Networks)
n-step SARSA: On-Policy TD Control
n-step Q-learning with Function Approximation
n-step Expected SARSA with Function Approximation
Baird’s counterexample: Semi-gradient Off-Policy TD(0) to demonstrate off-policy divergence
Experience Replay to stabilize training
Multi-armed Bandit Problem
- Boltzmann (Softmax)
- UCB
- Thomson sampling
- E-Greedy
Policy Iteration
Value Iteration
Q-learning: Off-Policy TD Control
Continuos Random Walk
Sparse Coarse Coding

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
BairdCounterExample.ipynb		BairdCounterExample.ipynb
Cartpole_REINFORCE.ipynb		Cartpole_REINFORCE.ipynb
Cartpole_REINFORCE_Actor_Critic.ipynb		Cartpole_REINFORCE_Actor_Critic.ipynb
Continuous_Random_Walk.ipynb		Continuous_Random_Walk.ipynb
Multi_Armed_Bandits_Algorithms.ipynb		Multi_Armed_Bandits_Algorithms.ipynb
Policy_and_Value_Iteration_Frozen_Lake.ipynb		Policy_and_Value_Iteration_Frozen_Lake.ipynb
Q_learning_CartPole.ipynb		Q_learning_CartPole.ipynb
README.md		README.md
n_step_q_learning_sarsa_and_expected_sarsa.ipynb		n_step_q_learning_sarsa_and_expected_sarsa.ipynb