def run_episode(env, agent, deterministic, do_training=True, rendering=False, max_timesteps=1000):
"""
This methods runs one episode for a gym environment.
deterministic == True => agent executes only greedy actions according the Q function approximator (no random actions).
do_training == True => train agent
"""
stats = EpisodeStats() # save statistics like episode reward or action usage
state = env.reset()
step = 0
while True:
action_id = agent.act(state=state, deterministic=deterministic)
next_state, reward, terminal, info = env.step(action_id)
if do_training:
agent.train(state, action_id, next_state, reward, terminal)
stats.step(reward, action_id)
state = next_state
if rendering:
env.render()
if terminal or step > max_timesteps:
break
step += 1
return stats
def run_episode(env, agent, deterministic, skip_frames=0, do_training=True, rendering=False, max_timesteps=1000, history_length=0):
"""
This methods runs one episode for a gym environment.
deterministic == True => agent executes only greedy actions according the Q function approximator (no random actions).
do_training == True => train agent
"""
stats = EpisodeStats()
# Save history
image_hist = []
step = 0
state = env.reset()
# fix bug of corrupted states without rendering in gym environment
env.viewer.window.dispatch_events()
# append image history to first state
state = state_preprocessing(state)
image_hist.extend([state] * (history_length + 1))
state = np.array(image_hist).reshape(96, 96, history_length + 1)
while True:
# TODO: get action_id from agent
# Hint: adapt the probabilities of the 5 actions for random sampling so that the agent explores properly.
# action_id = agent.act(...)
# action = your_id_to_action_method(...)
# Hint: frame skipping might help you to get better results.
reward = 0
for _ in range(skip_frames + 1):
next_state, r, terminal, info = env.step(action)
reward += r
if rendering:
env.render()
if terminal:
break
next_state = state_preprocessing(next_state)
image_hist.append(next_state)
image_hist.pop(0)
next_state = np.array(image_hist).reshape(96, 96, history_length + 1)
if do_training:
agent.train(state, action_id, next_state, reward, terminal)
stats.step(reward, action_id)
state = next_state
if terminal or (step * (skip_frames + 1)) > max_timesteps :
break
step += 1
return stats
