action = env.getActionSet()[action]
reward = env.act(action)
obs = list(env.getGameState().values())
episode_reward += reward
if render:
env.getScreenRGB()
if env.game_over():
break
eval_reward.append(episode_reward)
return np.mean(eval_reward)
env = Catcher(500, 500)
env = PLE(env, fps=10, display_screen=True, force_fps=False)
act_dim = len(env.getActionSet())
obs_dim = len(env.getGameState())
rpm = ReplayMemory(MEMORY_SIZE)
model = Model(act_dim=act_dim)
alg = DQN(model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(alg,
obs_dim=obs_dim,
act_dim=act_dim,
e_greed_decrement=0.1,
e_greed=1e-6)
"""
#添加经验池
while len(rpm) < MEMORY_WARMUP_SIZE:
run_episode(env, agent, rpm)
'fruit_y': int 0 - 300
}
Actions:
[97, 100, None]
'''
game = Catcher(width=256, height=256, init_lives=10)
p = PLE(game, fps=30, display_screen=True, force_fps=False)
p.init()
agent = RandomAgent(p.getActionSet())
nb_frames = 50000
reward = 0.0
print(game.getGameState())
print(p.getActionSet())
for f in range(nb_frames):
if p.game_over(): #check if the game is over
p.reset_game()
print("-----------------")
state0 = game.getGameState()
action_index = agent.pickAction(state0)
action = actionValue(action_index)
print(action)
fruit_reward = p.act(action)
state1 = game.getGameState()
reward = Reward(state0, state1, fruit_reward)
'''
State Formate:
{
'player_x': int,
'player_vel': float,
'fruit_x': int,
'fruit_y': int
}
Actions:
[97, 100, None]
'''
game = Catcher(width=256, height=256, init_lives=3)
p = PLE(game, fps=30, display_screen=True, force_fps=False)
p.init()
agent = RandomAgent(p.getActionSet())
nb_frames = 1000
reward = 0.0
print(game.getGameState())
print(p.getActionSet())
for f in range(nb_frames):
if p.game_over(): #check if the game is over
p.reset_game()
state = game.getGameState()
action = agent.pickAction(state, reward)
reward = p.act(action)
# initialize game
game = Catcher(width=100, height=100, init_lives=1)
p = PLE(game,
fps=30,
frame_skip=3,
num_steps=1,
force_fps=False,
display_screen=True)
p.init()
# initialize agent
agent = PlayingAgent(p.getActionSet(), model)
# run training
episodes = 10
max_timestamps = 300
episode_results = []
for episode_index in range(episodes):
p.reset_game()
for timestamp in range(max_timestamps):
observation = game.getGameState()
action = agent.pick_action(observation)
reward = p.act(action)
if p.game_over():
break
print('Episode %5d Timestamps: %d' % (episode_index + 1, timestamp + 1))