def simulate_random_agent(games):
data = {
"actions": [],
"rewards": np.zeros(NUM_STEPS)
}
for g in tqdm(range(NUM_GAMES), desc="Random Agent"):
agent = RandomAgent(NUM_ARMS, NUM_STEPS)
game = games[g]
actions, rewards = agent.play(game)
data["actions"].extend(actions)
data["rewards"] += rewards
# Convert sum to average reward per step.
data["rewards"] /= NUM_GAMES
return data
def play_random(params_path):
Parameters.load(params_path)
environment = Environment()
agent = RandomAgent(environment)
all_scores = agent.play()
print('mean: ', np.mean(all_scores), '\tstd: ', np.std(all_scores))
