rohithmukku / deep_rl

Implementations of Deep Reinforcement Learning Algorithms

Deep Reinforcement Learning Methods

This repository contains implementations of some of the popular DRL methods.

Methods

• Deep Q-Networks (DQN)

• Vanilla Policy Gradient (VPG)

• Vanilla Actor Critic (VAC)

• Advantage Actor Critic (A2C)

• Natural Policy Gradient (NPG)

• Proximal Policy Optimization (PPO)

• Deep Deterministic Policy Gradient (DDPG)

• Twin Delayed DDPG (TD3)

• Asynchronous Advantage Actor Critic (A3C)

• Soft Actor Critic (SAC)

  NOTE: These methods are not optimized. Only PPO, DDOG Work on continuous action space.

Environments

• Classic OpenAI Gym

  Environment	Observation Space	Action Space
  CartPole-v1	Box, 4	Discrete, 2
  Pendulum-v0	Box, 3	Box, 1
  MountainCar-v0	Box, 2	Discrete, 3
  MountainCarContinuous-v0	Box, 2	Box, 1
  Acrobot-v1	Box, 6	Discrete, 3

• MuJoCo

  Environment	Observation Space	Action Space
  Ant-v2	Box, 111	Box, 8
  HalfCheetah-v2	Box, 17	Box, 6
  Hopper-v2	Box, 11	Box, 3
  Humanoid-v2	Box, 376	Box, 17
  HumanoidStandup-v2	Box, 376	Box, 17
  InvertedDoublePendulum-v2	Box, 11	Box, 1
  InvertedPendulum-v2	Box, 4	Box, 1
  Reacher-v2	Box, 11	Box, 2
  Swimmer-v2	Box, 8	Box, 2
  Walker2d-v2	Box, 17	Box, 6

• Robotics (Not implemented)

Installation

For MuJoCo installation, refer to these links:

• https://github.com/openai/mujoco-py

Usage

$ python main.py --help

main is powered by Hydra.

== Configuration groups ==
Compose your configuration from those groups (group=option)

agent: dqn, vpg


== Config ==
Override anything in the config (foo.bar=value)

agent:
  _target_: agent.dqn.DQNAgent
  obs_dim: ???
  act_dim: ???
  buffer_size: 10000
  min_size: 100
  batch_size: 32
  epsilon: 1
  epsilon_decay: 0.95
  min_epsilon: 0.01
  gamma: 0.99
  alpha: 0.001
  target_update: 100
env: CartPole-v0
num_episodes: 1000
device: cuda
plot: false


Powered by Hydra (https://hydra.cc)
Use --hydra-help to view Hydra specific help

References

Blogs/Tutorials

• https://jaromiru.com/2016/09/27/lets-make-a-dqn-theory/
• https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
• https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html
• https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html

Papers

• DQN
• A2C
• PPO
• DDPG

Code

• https://github.com/dongminlee94/deep_rl
• https://github.com/openai/spinningup/tree/master/spinup/algos/pytorch
• https://github.com/nikhilbarhate99/PPO-PyTorch

Todo

• Implement SAC
• Unit testing
• Better logs, plots