RL_collects

experiments of reinforcement learning algorithms

vallinaPG

• Implemented for discrete action space
• Basic policy gradient with monte-carlo return
• To try 2 forms of Policy gradient theorem, modify Actor._discount_and_norm_rewards()in policy_gradient.py
  • Current version: Rewards after action (equivalent one, less variance)
  • Uncomment: total rewards of episode (basic version of formula)
• Use moving average of episdoes' return as basline
• Run run_CartPole.py to play cartPole balancing game in OpenAI gym.

PPO

• use clipped surrogate objective
• baseline given by state value approximated by critor
• PPO class to be implemented
• based on this paper