akashe / DeepReinforcementLearning

Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.

Deep RL algorithms implemented using Pytorch

Algo list:

1. DQN
2. Vanilla policy Gradient
3. Deep Deterministic Policy Gradient
4. Twin Delayed Deep Deterministic Policy Gradient
5. Soft Actor Critic
6. Proximal Policy Optimization - CLIP

Article on deeper Look into policy gradients

Experimental Results:

Algorithm	Discrete Env: LunarLander-v2	Continuous Env: Pendulum-v0
DQN		-
VPG		-
DDPG	-
TD3	-
SAC	-
PPO	-

Usage:

Just run the file/algorithm directly. There is no common structures between algorithms as I implemented them as I learnt them. Different algorithms are inspired from different sources.

Resources:

1. RL course by David Silver
2. Lecture slides for above course
3. Spinning up by OpenAI
4. More exhaustive RL guide by Deeny Britz

Future projects:

1. If time available I will add a simple program for elevator using RL.
2. Better graphs