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