### Deep RL algorithms implemented using Pytorch
#### Algo list:
1. [DQN](https://github.com/akashe/DeepReinforcementLearning/blob/main/DQN.py)
2. [Vanilla policy Gradient](https://github.com/akashe/DeepReinforcementLearning/blob/main/vanilla_policy_gradient.py)
3. [Deep Deterministic Policy Gradient](https://github.com/akashe/DeepReinforcementLearning/blob/main/ddpg.py)
4. [Twin Delayed Deep Deterministic Policy Gradient](https://github.com/akashe/DeepReinforcementLearning/blob/main/td3.py)
5. [Soft Actor Critic](https://github.com/akashe/DeepReinforcementLearning/blob/main/SoftActorCritic.py)
6. [Proximal Policy Optimization - CLIP](https://github.com/akashe/DeepReinforcementLearning/blob/main/ppo_clip.py)
###### Article on deeper Look into [policy gradients](https://akashe.io/blog/2020/10/14/policy-gradient-methods/)
#### 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](https://www.youtube.com/watch?v=KHZVXao4qXs&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ&index=7)
2. [Lecture slides for above course](https://www.davidsilver.uk/teaching/)
3. [Spinning up by OpenAI](https://spinningup.openai.com)
3. [More exhaustive RL guide by Deeny Britz](https://github.com/dennybritz/reinforcement-learning)
#### Future projects:
1. If time available I will add a simple program for elevator using RL.
2. Better graphs
DeepReinforcementLearning：深度RL实施。 在pytorch中实现的DQN，SAC，DDPG，TD3，P...
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2021-02-13
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DeepReinforcementLearning-main.zip （42个子文件） 
DeepReinforcementLearning-main 
td3.py 4KB .ipynb_checkpoints test_and_intial_Experimentation-checkpoint.ipynb 72B Policy Gradient Methods-checkpoint.ipynb 13KB RLUtils __init__.py 21B utils.py 3KB __pycache__ utils.cpython-37.pyc 4KB __init__.cpython-37.pyc 179B SoftActorCritic.py 3KB Policy Gradient Methods.ipynb 13KB Readme.md 2KB .idea .gitignore 47B misc.xml 292B vcs.xml 180B inspectionProfiles Project_Default.xml 659B profiles_settings.xml 174B modules.xml 294B ReinforcementLearning.iml 317B ppo_clip.py 4KB ddpg.py 9KB agents __init__.py 57B agent.py 125B __pycache__ __init__.cpython-37.pyc 225B agent.cpython-37.pyc 560B ActorCriticAgents __init__.py 63B PPO_clip_agent.py 11KB td3_agent.py 7KB soft_Actor_critic_Agent.py 7KB __pycache__ soft_Actor_critic_Agent.cpython-37.pyc 6KB td3_agent.cpython-37.pyc 6KB PPO_clip_agent.cpython-37.pyc 8KB __init__.cpython-37.pyc 235B MLPAgent.py 0B figures 
PPO_MountainCarContinuous-v0_rewards.png 22KB DQN_Lunar_lander_losses.png 38KB VPG_LunarLander-v2_rewards.png 38KB SAC_Pendulum-v0_rewards.png 51KB DQN_Lunar_lander_rewards.png 48KB TD3_Pendulum_rewards.png 62KB DDPG_Pendulum-v0_rewards.png 43KB PPO_Pendulum-v0_rewards.png 57KB vanilla_policy_gradient.py 8KB DQN.py 19KB
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