def evaluate(env, config, q_table, episode, render=False, output=True):
"""
Evaluate configuration of SARSA on given environment initialised with given Q-table
:param env (gym.Env): environment to execute evaluation on
:param config (Dict[str, float]): configuration dictionary containing hyperparameters
:param q_table (Dict[(Obs, Act), float]): Q-table mapping observation-action to Q-values
:param episode (int): episodes of training completed
:param render (bool): flag whether evaluation runs should be rendered
:param output (bool): flag whether mean evaluation performance should be printed
:return (float, float): mean and standard deviation of reward received over episodes
"""
eval_agent = SARSA(
num_acts=env.action_space.n,
gamma=config["gamma"],
epsilon=0.0,
alpha=config["alpha"],
)
eval_agent.q_table = q_table
episodic_rewards = []
for eps_num in range(config["eval_episodes"]):
obs = env.reset()
if render:
env.render()
sleep(1)
episodic_reward = 0
done = False
steps = 0
while not done and steps <= config["max_episode_steps"]:
steps += 1
act = eval_agent.act(obs)
n_obs, reward, done, info = env.step(act)
if render:
env.render()
sleep(1)
episodic_reward += reward
obs = n_obs
episodic_rewards.append(episodic_reward)
mean_reward = np.mean(episodic_rewards)
std_reward = np.std(episodic_rewards)
if output:
print(
f"EVALUATION ({episode}/{CONFIG['total_eps']}): MEAN REWARD OF {mean_reward}"
)
if mean_reward >= 0.9:
print(f"EVALUATION: SOLVED")
else:
print(f"EVALUATION: NOT SOLVED!")
return mean_reward, std_reward
def evaluate(env, config, q_table, render=False):
"""
Evaluate configuration of SARSA on given environment initialised with given Q-table
:param env (gym.Env): environment to execute evaluation on
:param config (Dict[str, float]): configuration dictionary containing hyperparameters
:param q_table (Dict[(Obs, Act), float]): Q-table mapping observation-action to Q-values
:param render (bool): flag whether evaluation runs should be rendered
:return (float, float, int): mean and standard deviation of return received over episodes, number
of negative returns
"""
eval_agent = SARSA(
num_acts=env.action_space.n,
gamma=config["gamma"],
epsilon=0.0,
alpha=config["alpha"],
)
eval_agent.q_table = q_table
episodic_returns = []
for eps_num in range(config["eval_episodes"]):
obs = env.reset()
if render:
env.render()
sleep(1)
episodic_return = 0
done = False
steps = 0
while not done and steps <= config["max_episode_steps"]:
steps += 1
act = eval_agent.act(obs)
n_obs, reward, done, info = env.step(act)
if render:
env.render()
sleep(1)
episodic_return += reward
obs = n_obs
episodic_returns.append(episodic_return)
mean_return = np.mean(episodic_returns)
std_return = np.std(episodic_returns)
negative_returns = sum([ret < 0 for ret in episodic_returns])
return mean_return, std_return, negative_returns