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PPO_ProcgenGame.py
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PPO_ProcgenGame.py
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import torch
from agents import TYPE
from agents.PPOProcgenAgent import PPOProcgenCNDAgent, PPOProcgenAgent, PPOProcgenRNDAgent, PPOProcgenQRNDAgent, PPOProcgenSRRNDAgent, PPOProcgenFEDRefAgent, PPOProcgenFWDAgent, PPOProcgenICMAgent
from experiment.ppo_nenv_experiment import ExperimentNEnvPPO
from utils.MultiEnvWrapper import MultiEnvParallel
from utils.ProcgenWrapper import WrapperProcgenExploration
def encode_state(state):
return torch.tensor(state, dtype=torch.float32)
def test(config, path, env_name):
env = WrapperProcgenExploration(env_name)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenCNDAgent(input_shape, action_dim, config, TYPE.discrete)
agent.load(path)
experiment.test(agent)
env.close()
def run_baseline(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_baseline(agent, trial)
env.close()
def run_rnd_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenRNDAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_rnd_model(agent, trial)
env.close()
def run_qrnd_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenQRNDAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_qrnd_model(agent, trial)
env.close()
def run_sr_rnd_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenSRRNDAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_sr_rnd_model(agent, trial)
env.close()
def run_cnd_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenCNDAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_cnd_model(agent, trial)
env.close()
# def run_dop_model(config, trial, env_name):
# print('Creating {0:d} environments'.format(config.n_env))
# env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
#
# input_shape = env.observation_space.shape
# action_dim = env.action_space.n
#
# print('Start training')
# experiment = ExperimentNEnvPPO(env_name, env, config)
#
# experiment.add_preprocess(encode_state)
# agent = PPOProcgenDOPAgent(input_shape, action_dim, config, TYPE.discrete)
# experiment.run_dop_model(agent, trial)
#
# env.close()
def run_fed_ref_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenFEDRefAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_fed_ref_model(agent, trial)
env.close()
# def run_forward_model(config, trial, env_name):
# print('Creating {0:d} environments'.format(config.n_env))
# env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
#
# input_shape = env.observation_space.shape
# action_dim = env.action_space.n
#
# print('Start training')
# experiment = ExperimentNEnvPPO(env_name, env, config)
#
# experiment.add_preprocess(encode_state)
# agent = PPOProcgenForwardModelAgent(input_shape, action_dim, config, TYPE.discrete)
# experiment.run_forward_model(agent, trial)
#
# env.close()
def run_fwd_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenFWDAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_fwd_model(agent, trial)
env.close()
def run_icm_model(config, trial, env_name):
print('Creating {0:d} environments'.format(config.n_env))
env = MultiEnvParallel([WrapperProcgenExploration(env_name) for _ in range(config.n_env)], config.n_env, config.num_threads)
input_shape = env.observation_space.shape
action_dim = env.action_space.n
print('Start training')
experiment = ExperimentNEnvPPO(env_name, env, config)
experiment.add_preprocess(encode_state)
agent = PPOProcgenICMAgent(input_shape, action_dim, config, TYPE.discrete)
experiment.run_icm_model(agent, trial)
env.close()