Model-free Deep Reinforcement Learning Algorithms Implementation

Overview

This is a repository about the implementation of many model-free deep reinforcement learning algorithms.

• Policy gradient ( On-Policy, Monte Carlo, Continuous Action Space )
• Actor-Critic ( Off-Policy, Temporal Difference, Continuous Action Space )
• Deep Q-Learning ( Off-Policy, Temporal Difference, Discrete Action Space )
• Deep Deterministic Policy Gradient ( Off-Policy, Temporal Difference, Continuous Action Space )
• Twin Delayed Deep Deterministic Policy Gradient ( Off-Policy, Temporal Difference, Continuous Action Space )
• Proximal Policy Optimization ( On-Policy, Monte Carlo, Discrete Action Space )

Usage

Test :

python algos/DDPG/main.py --test

Train:

python algos/DDPG/main.py --train