# Setup agents # ################# players[0] = DQNAgent( player_num=0, map_name=map_name, train=False, network_save_name=None, network_load_name=None, ) names[0] = "DQN Agent" # Create an array of all agents that could be used during training opposing_agents = [ { 'name': 'Random Agent Delay', 'agent': random_actions_delay(env.num_actions_per_turn, 1, map_name) }, { 'name': 'Random Agent', 'agent': random_actions(env.num_actions_per_turn, 1, map_name) }, { 'name': 'Bull Rush', 'agent': bull_rush(env.num_actions_per_turn, 1) }, { 'name': 'All Cycle', 'agent': all_cycle(env.num_actions_per_turn, 1) }, { 'name': 'Base Rush v1',
env = gym.make('everglades-v0') players = {} names = {} ################# # Setup agents # ################# players[0] = DQNAgent( player_num=0, map_name=map_name, train=TRAIN, network_save_name='./agents/Smart_State/saved_models/local', network_load_name=None, ) names[0] = "DQN Agent" players[1] = random_actions_delay(env.num_actions_per_turn, 1, map_name) names[1] = 'Random Agent Delay' ################# actions = {} ## Set high episode to test convergence # Change back to resonable setting for other testing n_episodes = 2500 ######################### # Statistic variables # ######################### k = 100 stats = AgentStatistics(names[0], n_episodes,