gym-td3-keras

Reference Code : gym-ddpg-keras(DDPG)

Keras Implementation of TD3(Twin Delayed Deep Deterministic Policy Gradient) with PER(Prioritized Experience Replay) option on OpenAI gym framework

STATUS : IN PROGRESS

This branch is just for debugging, change the branch to main.

To do

Test on Simulation

• [ ] RoboschoolInvertedPendulum-v1
• [ ] RoboschoolHopper-v1
• [ ] RoboschoolHalfCheetah-v1
• [ ] RoboschoolAnt-v1

Experiment Details from paper

Network Model & Hyperparameter

• For our implementation of DDPG, we use a two layer feedforward neural network of 400 and 300 hidden nodes respectively, with rectified linear units (ReLU) between each layer for both the actor and critic, and a final tanh unit following the output of the actor.
• Unlike the original DDPG, the critic receives both the state and action as input to the first layer.
• Both network parameters are updated using Adam with a learning rate of 10−3.
• After each time step, the networks are trained with a mini-batch of a 100 transitions, sampled uniformly from a replay buffer containing the entire history of the agent.
• Both target networks are updated with τ = 0.005.

Differences from DDPG

• The target policy smoothing is implemented by adding to the actions chosen by the target actor network, clipped to (−0.5, 0.5).

• Delayed policy updates consists of only updating the actor and target critic network every d iterations, with d = 2.

  (While a larger d would result in a larger benefit with respect to accumulating errors, for fair comparison, the critics are only trained once per time step, and training the actor for too few iterations would cripple learning.)

Exploration

• To remove the dependency on the initial parameters of the policy we use a purely exploratory policy for the first 10000 time steps of stable length environments.

• Afterwards, we use an off-policy exploration strategy, adding Gaussian noise N (0, 0.1) to each action.

  (we found noise drawn from the Ornstein-Uhlenbeck process offered no performance benefits.)

Evaluation

• Each task is run for 1 million time steps with evaluations every 5000 time steps, where each evaluation reports the average reward over 10 episodes with no exploration noise.

Easy Installation

1. Make an independent environment using virtualenv

# install virtualenv module
sudo apt-get install python3-pip
sudo pip3 install virtualenv

# create a virtual environment named venv
virtualenv venv 

# activate the environment
source venv/bin/activate 

​ To escape the environment, deactivate

2. Install the requirements

pip install -r requirements.txt

3. Run the training node

#trainnig
python train.py

Reference

[1] Addressing Function Approximation Error in Actor-Critic Methods

@misc{fujimoto2018addressing,
      title={Addressing Function Approximation Error in Actor-Critic Methods}, 
      author={Scott Fujimoto and Herke van Hoof and David Meger},
      year={2018},
      eprint={1802.09477},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

REVIEW | PAPER

[2] CUN-bjy/gym-ddpg-keras

[3] sfujim/TD3

[4] quantumiracle/SOTA-RL-Algorithms