import marl # Check available agents print("\n| Agents\t\t", list(marl.agent.available())) # Check available agents print("\n| Policies\t\t", list(marl.policy.available())) # Check available agents print("\n| Models\t\t", list(marl.model.available())) # Check available exploration process print("\n| Expl. Processes\t", list(marl.exploration.available())) # Check available experience memory print("\n| Experience Memory\t", list(marl.experience.available()))

import marl from marl.agent import DQNAgent from marl.model.nn import MlpNet import gym env = gym.make("LunarLander-v2") obs_s = env.observation_space act_s = env.action_space mlp_model = MlpNet(8,4, hidden_size=[64, 32]) dqn_agent = DQNAgent(mlp_model, obs_s, act_s, experience="ReplayMemory-5000", exploration="EpsGreedy", lr=0.001, name="DQN-LunarLander") # Train the agent for 100 000 timesteps dqn_agent.learn(env, nb_timesteps=100000) # Test the agent for 10 episodes dqn_agent.test(env, nb_episodes=10)

import marl from marl import MARL from marl.agent import MinimaxQAgent from marl.exploration import EpsGreedy from soccer import DiscreteSoccerEnv # Environment available here "https://github.com/blavad/soccer" env = DiscreteSoccerEnv(nb_pl_team1=1, nb_pl_team2=1) obs_s = env.observation_space act_s = env.action_space # Custom exploration process expl1 = EpsGreedy(eps_deb=1.,eps_fin=.3) expl2 = EpsGreedy(eps_deb=1.,eps_fin=.3) # Create two minimax-Q agents q_agent1 = MinimaxQAgent(obs_s, act_s, act_s, exploration=expl1, gamma=0.9, lr=0.001, name="SoccerJ1") q_agent2 = MinimaxQAgent(obs_s, act_s, act_s, exploration=expl2, gamma=0.9, lr=0.001, name="SoccerJ2") # Create the trainable multi-agent system mas = MARL(agents_list=[q_agent1, q_agent2]) # Assign MAS to each agent q_agent1.set_mas(mas) q_agent2.set_mas(mas) # Train the agent for 100 000 timesteps mas.learn(env, nb_timesteps=100000) # Test the agents for 10 episodes mas.test(env, nb_episodes=10, time_laps=0.5)