print('Number of agents:', num_agents)
# size of each action
action_size = brain.vector_action_space_size
print('Size of each action:', action_size)
# examine the state space
states = env_info.vector_observations
state_size = states.shape[1]
print('There are {} agents. Each observes a state with length: {}'.format(
states.shape[0], state_size))
print('The state for the first agent looks like:', states[0])
agent_1 = Agent(state_size=state_size, action_size=action_size, random_seed=2)
agent_2 = Agent(state_size=state_size, action_size=action_size, random_seed=3)
agent_2.memory = agent_1.memory
agent_2.actor_local = agent_1.actor_local
agent_2.actor_target = agent_1.actor_target
agent_2.critic_local = agent_1.critic_local
agent_2.critic_target = agent_1.critic_target
t_max = 1000
print_every = 100
maxlen = 100
score = []
ev_score = []
scores_deque = deque(maxlen=maxlen)
for i_episode in range(1, env.n_episodes + 1): # play game for 5 episodes
env_info = env.reset(train_mode=True)[brain_name] # reset the environment
states = env_info.vector_observations # get the current state (for each agent)
scores = np.zeros(num_agents) # initialize the score (for each agent)
env = UnityEnvWrapper(no_graphics=False)
agent1 = Agent(state_size=env.n_states() + 1,
action_size=env.n_actions(), random_seed=2)
agent2 = Agent(state_size=env.n_states() + 1,
action_size=env.n_actions(), random_seed=2)
agent2.critic_local = agent1.critic_local
agent2.critic_target = agent1.critic_target
agent2.critic_optimizer = agent1.critic_optimizer
agent2.actor_local = agent1.actor_local
agent2.actor_target = agent1.actor_target
agent2.actor_optimizer = agent1.actor_optimizer
agent2.memory = agent1.memory
print(env.n_agents(), env.n_states(), env.n_actions())
def save(i_episode, scores1, scores2, mean_scores):
print("Saving checkpoints...")
torch.save(agent1.actor_local.state_dict(),
'checkpoint_actor_1.pth')
torch.save(agent2.actor_local.state_dict(),
'checkpoint_actor_2.pth')
torch.save(agent1.critic_local.state_dict(),
'checkpoint_critic_1.pth')
torch.save(agent2.critic_local.state_dict(),
'checkpoint_critic_2.pth')
torch.save(dict(episode=i_episode,
