run_random_actions.py

from unityagents import UnityEnvironment
import numpy as np

env = UnityEnvironment(file_name="./Tennis.app")

# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]

# reset the environment
env_info = env.reset(train_mode=False)[brain_name]

# number of agents
num_agents = len(env_info.agents)
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])

for _ in range(5):  # play game for 5 episodes
    env_info = env.reset(train_mode=False)[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)
    while True:
        actions = np.random.randn(
            num_agents, action_size
        )  # select an action (for each agent)
        actions = np.clip(actions, -1, 1)  # all actions between -1 and 1
        env_info = env.step(actions)[brain_name]  # send all actions to tne environment
        next_states = env_info.vector_observations  # get next state (for each agent)
        rewards = env_info.rewards  # get reward (for each agent)
        dones = env_info.local_done  # see if episode finished
        scores += env_info.rewards  # update the score (for each agent)
        states = next_states  # roll over states to next time step
        if np.any(dones):  # exit loop if episode finished
            break
    print("Total score (averaged over agents) this episode: {}".format(np.mean(scores)))

env.close()