Trajectory Planning using HER & Reward Engineering

Trajectory planning based on Reinforcement Learning with Hindsight Experience Replay, Prioritized Experience Replay & Dense Reward Engineering to solve openai-gym robotics "FetchReach-v1" environment using PyTorch & Tensorflow2.

Reinforcement Learning Algorithms

1. Dense Reward Engineering: Engineered vector based distance measure to replace sparse rewards.

2. Hindsight Experience Relay (HER): Implemented HER Future Strategy based goal sampling for buffer augmentation.

3. Prioritized Experience Relay (PER): Samples and optimizes the past experiences ended with errors to get better future rewards.

Agent Profiles

1. Dense Reward Engineering

   DDPG Agent
   PER + DDPG Agent

2. Hindsight Experience Repay

   DDPG Agent
   PER + DDPG Agent

Play Preview

• Previews from older implementation in TF2.

  Dense Rewards	HER

Dependencies

Install dependencies using:

pip3 install -r requirements.txt 

1. Additionally install 'mujoco_py' according to 'https://github.com/openai/mujoco-py'

Developer

• Name: Kanishk Navale
• Email: navalekanishk@gmail.com
• Website: https://kanishknavale.github.io/