def run_single_episode(env: UnityEnvironment,
brain_name,
agent: Agent = None,
train_mode=False,
max_t=2000,
epsilon=0.0):
"""
Execute a single episode
Params
======
env (UnityEnvironment): enviroment
brain_name (string): default brain name
agent (Agent): agent that is responsible for control the actions (if no agent, a random action is chosen)
train_mode (bool): indicate if the environment is on the train mode
max_t (int): max number of steps in each episode
epsilon (float): attenuate the noise applied to the action
Return
======
scores (float): episode scores of all agents
"""
env_info = env.reset(train_mode=train_mode)[brain_name]
num_agents = len(env_info.agents)
action_size = env.brains[brain_name].vector_action_space_size
states = env_info.vector_observations
scores = np.zeros(num_agents) # initialize the score (for each agent)
# Run all the steps of one episode
for time_step in range(1, max_t + 1):
if agent: # if a agent is provide, get all the action (for each agent)
actions = agent.act(states, epsilon=epsilon, add_noise=train_mode)
else: # select a random action (if no agent)
actions = np.random.randn(num_agents, action_size)
actions = np.clip(actions, -1, 1) # all actions between -1 and 1
env_info = env.step(actions)[
brain_name] # send the action to the environment
next_states = env_info.vector_observations # get the next state
rewards = env_info.rewards # get the reward
dones = env_info.local_done # get the done flag
if agent and train_mode: # learn if in train mode
agent.step(states, actions, rewards, next_states, dones, time_step)
states = next_states
scores += rewards # increase the scores (for each agent)
if np.any(dones): # Exit episode if done
break
return scores
def run_single_episode(env: UnityEnvironment,
brain_name,
agent: Agent = None,
max_t=1000,
eps=0.,
train_mode=False):
"""
Execute a single episode
Params
======
env (UnityEnvironment): enviroment
brain_name (string): default brain name
agent (Agent): agent that is responsible for control the actions (if no agent, a random action is chosen)
max_t (int): max steps in each episode
train_mode (bool): indicate if the environment is on the train mode
Return
======
score (float): total score of episode
"""
env_info = env.reset(train_mode=train_mode)[brain_name]
action_size = env.brains[brain_name].vector_action_space_size
state = env_info.vector_observations[0]
score = 0
for _ in range(max_t): # Run each step in episode
action = agent.act(state,
eps) if agent else np.random.randint(action_size)
env_info = env.step(action)[
brain_name] # send the action to the environment
next_state = env_info.vector_observations[0] # get the next state
reward = env_info.rewards[0] # get the reward
done = env_info.local_done[0] # get the done flag
if agent and train_mode:
agent.step(state, action, reward, next_state, done)
state = next_state
score += reward
if done: # Exit episode if done
break
return score
