def test_agents_can_play_games_of_different_dimensions():
config.num_episodes_to_run = 10
config.hyperparameters["DQN_Agents"]["batch_size"] = 3
AGENTS = [
A2C, A3C, PPO, DDQN, DQN_With_Fixed_Q_Targets,
DDQN_With_Prioritised_Experience_Replay, DQN
]
trainer = Trainer(config, AGENTS)
config.environment = gym.make("CartPole-v0")
results = trainer.run_games_for_agents()
for agent in AGENTS:
assert agent.agent_name in results.keys()
AGENTS = [SAC, TD3, PPO, DDPG]
config.environment = gym.make("MountainCarContinuous-v0")
trainer = Trainer(config, AGENTS)
results = trainer.run_games_for_agents()
for agent in AGENTS:
assert agent.agent_name in results.keys()
AGENTS = [DDQN, SNN_HRL]
config.environment = Four_Rooms_Environment(
15,
15,
stochastic_actions_probability=0.25,
random_start_user_place=True,
random_goal_place=False)
trainer = Trainer(config, AGENTS)
results = trainer.run_games_for_agents()
for agent in AGENTS:
assert agent.agent_name in results.keys()
def run_train(args):
save_path = os.path.join(args.logdir, args.agent+'@cps'+str(args.stage))
args.save_path = init_trial_path(save_path)
with open(os.path.join(args.save_path, 'args.json'), 'w') as f:
json.dump(args.__dict__, f)
print(args)
config=init_config(args)
agent = get_agent(args.agent)
trainer = Trainer(config, [agent])
results = trainer.run_games_for_agents()
agent_name = agent.agent_name
res_age=np.array(results[agent_name][0][0])
score = (max(res_age)+np.mean(res_age[-10:]))/2
converge_step_1=np.where(res_age>0)[0]
converge_step_2= np.where(res_age > 100)[0]
if len(converge_step_1)>0:
converge_step_1 =converge_step_1[0]
else:
converge_step_1 = -1
if len(converge_step_2)>0:
converge_step_2 =converge_step_2[0]
else:
converge_step_2 = -1
print("Train Done")
print(f"Agent={agent_name}, Score={score}, Path={args.save_path}")
print(f"Final Reward={np.mean(res_age[-10:])}, Converge Step 0 ={converge_step_1}, Converge Step 100 ={converge_step_2},")
agent = trainer.trained_agents[0][0]
return agent
def test_agent_solve_bit_flipping_game():
AGENTS = [DQN]
trainer = Trainer(config, AGENTS)
results = trainer.run_games_for_agents()
for agent in AGENTS:
agent_results = results[agent.agent_name]
agent_results = np.max(agent_results[0][1][50:])
assert agent_results >= 0.0, "Failed for {} -- score {}".format(
agent.agent_name, agent_results)
def test_agent_solve_bit_flipping_game():
AGENTS = [
PPO, DDQN, DQN_With_Fixed_Q_Targets,
DDQN_With_Prioritised_Experience_Replay, DQN, DQN_HER
]
trainer = Trainer(config, AGENTS)
results = trainer.run_games_for_agents()
for agent in AGENTS:
agent_results = results[agent.agent_name]
agent_results = np.max(agent_results[0][1][50:])
assert agent_results >= 0.0, "Failed for {} -- score {}".format(
agent.agent_name, agent_results)
"batch_norm": False,
"buffer_size": 1000000,
"tau": 0.005,
"gradient_clipping_norm": 5,
"initialiser": "Xavier"
},
"min_steps_before_learning": 400,
"batch_size": 64,
"discount_rate": 0.99,
"mu": 0.0, #for O-H noise
"theta": 0.15, #for O-H noise
"sigma": 0.25, #for O-H noise
"action_noise_std": 0.2, # for TD3
"action_noise_clipping_range": 0.5, # for TD3
"update_every_n_steps": 1,
"learning_updates_per_learning_session": 1,
"automatically_tune_entropy_hyperparameter": True,
"entropy_term_weight": None,
"add_extra_noise": False,
"do_evaluation_iterations": True
}
}
if __name__ == "__main__":
# turn it on if you want to test specific network saved
test = False
dictPath = None
AGENTS = [SAC_Discrete]
trainer = Trainer(config, AGENTS)
trainer.run_games_for_agents(test, dictPath)
"batch_norm": False,
"tau": 0.01,
"gradient_clipping_norm": 5
},
"Critic": {
"learning_rate": 0.01,
"linear_hidden_units": [400, 300],
"final_layer_activation": "None",
"batch_norm": False,
"buffer_size": 100000,
"tau": 0.01,
"gradient_clipping_norm": 5
},
"batch_size": 64,
"discount_rate": 0.99,
"mu": 0.0, # for O-H noise
"theta": 0.15, # for O-H noise
"sigma": 0.2, # for O-H noise
"action_noise_std": 0.2, # for TD3
"action_noise_clipping_range": 0.5, # for TD3
"update_every_n_steps": 1,
"learning_updates_per_learning_session": 1,
"clip_rewards": False
}
}
if __name__ == "__main__":
AGENTS = [DDPG, HIRO]
trainer = Trainer(config, AGENTS)
trainer.run_games_for_agents()
