Implementation of Continuous Control RL Algorithms.

Repository used for our paper Actor-Expert: A Framework for using Q-learning in Continuous Action Spaces.

webpage: https://sites.google.com/ualberta.ca/actorexpert

• Q-learning methods

  • Actor-Expert, Actor-Expert+: Actor-Expert: A Framework for using Q-learning in Continuous Action Spaces
  • Actor-Expert with PICNN
  • Wire-Fitting: Reinforcement Learning with High-dimensional, Continuous Actions
  • Normalized Advantage Functions(NAF): Continuous Deep Q-Learning with Model-based Acceleration
  • (Partial) Input Convex Neural Networks(PICNN): Input Convex Neural Networks - adapted from github.com/locuslab/icnn
  • QT-Opt: QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation - both single and mixture gaussian

• Policy Gradient methods

  • Deep Deterministic Policy Gradient(DDPG): Continuous Control with Deep Reinforcement Learning
  • Advantage Actor-Critic baseline with Replay Buffer: Not to be confused with ACER

Create virtual environment and install necessary packages through "pip3 -r requirements.txt"

Settings for available environments and agents are provided in jsonfiles/ directory

Example:

ENV=Pendulum-v0 (must match jsonfiles/environment/*.json name)

AGENT=ddpg (must match jsonfiles/agent/*.json name)

INDEX=0 (useful for running sweeps over different settings and doing multiple runs)

Run: python3 main.py --env_json jsonfiles/environment/$ENV.json --agent_json jsonfiles/agent/$AGENT.json --index $INDEX

(--render and --monitor is optional, to visualize/monitor the agents' training, only available for openai gym or mujoco environments. --write_plot is also available to plot the learned action-values and policy on Bimodal1DEnv domain.)

• ENV.json is used to specify evaluation settings:

  • TotalMilSteps: Total training steps to be run (in million)
  • EpisodeSteps: Steps in an episode (Use -1 to use the default setting)
  • EvalIntervalMilSteps: Evaluation Interval steps during training (in million)
  • EvalEpisodes: Number of episodes to evaluate in a single evaluation

• AGENT.json is used to specify agent hyperparameter settings:

  • norm: type of normalization to use
  • exploration_policy: "ou_noise", "none": Use "none" if the algorithm has its own exploration mechanism
  • actor/critic l1_dim, l2_dim: layer dimensions
  • learning rate
  • other algorithm specific settings