Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL)

DQN implementation for two multi-agent environments: agents_landmarks and predators_prey (See details.pdf for a detailed description of these environments).

Code structure

• ./environments/: folder where the two environments (agents_landmarks and predators_prey) are stored.
  1. ./environments/agents_landmarks: in this environment, there exist n agents that must cooperate through actions to reach a set of n landmarks in a two dimensional discrete k-by-k grid environment.
  2. ./environments/predators_prey: in this environment, n agents (called predators) must cooperate with each other to capture one prey in a two dimensional discrete k-by-k grid environment.
• ./dqn_agent.py: contains code for the implementation of DQN and its extensions (Double DQN, Dueling DQN, DQN with Prioritized Experience Replay) (See details.pdf for a detailed description of the DQN and its extensions).
• ./brain.py: contains code for the implementation of neural networks required for DQN (See details.pdf for a detailed description of the neural network implementation).
• ./uniform_experience_replay.py: contains code for the implementation of Uniform Experience Replay (UER) which can be used in DQN.
• ./prioritized_experience_replay.py: contains code for the implementation of Prioritized Experience Replay (PER) which can be used in DQN.
• ./sum_tree.py: contains code for the implementation of sum tree data structure which is used in Prioritized Experience Replay (PER).
• ./agents_landmarks_multiagent.py: contains code for applying DQN to the agents_landmarks environment.
• ./predators_prey_multiagent.py: contains code for applying DQN to the predators_prey environment.
• ./results_agents_landmarks/: folder where the results (neural net weights, rewards of the episodes, videos, figures, etc.) for the agents_landmarks environment are stored.
• ./results_predators_prey/: folder where the results (neural net weights, rewards of the episodes, videos, figures, etc.) for the predators_prey environment are stored.
• ./details.pdf: a pdf file including a detailed description of the DQN and its extensions, the environments, and the neural network implementation.

Results

Predators and Prey Environment

In this environment, the prey is captured when one predator moves to the location of the prey while the other predators occupy, for support, the neighboring cells of the prey's location.

Fixed prey (mode 0)

Random prey (mode 1)

Random escaping prey (mode 2)

Agents and Landmarks Environment

10 agents and 10 landmarks

16 agents and 16 landmarks

Todos

• Write required dependencies and installation steps
• ...