def make_policy_network(args):
"""Return the policy network for this run."""
kwargs = args.policy_kwargs
if args.environment == 'LunarLander-v2':
assert "output_dim" not in kwargs
policy_network = MultilayerPerceptron(4, **kwargs)
batch = np.zeros((1, 8), dtype=np.float32)
elif args.environment in ['CartPole-v0', 'CartPole-v1']:
assert "output_dim" not in kwargs
policy_network = MultilayerPerceptron(2, **kwargs)
batch = np.zeros((1, 4), dtype=np.float32)
else:
if args.policy_model == 'pmlp':
policy_network = ParallelMultilayerPerceptron(**args.policy_kwargs)
elif args.policy_model == 'apmlp':
policy_network = AttentionPMLP(**args.policy_kwargs)
elif args.policy_model == 'tpmlp':
policy_network = TransformerPMLP(**args.policy_kwargs)
else:
policy_network = PointerNetwork(**args.policy_kwargs)
batch = np.zeros(
(1, 10, 2 * args.k * int(args.distribution.split('-')[0])),
dtype=np.int32)
policy_network(batch) # build network
if args.policy_weights != "":
policy_network.load_weights(args.policy_weights)
return policy_network
def make_value_network(args):
"""Return the value network for this run."""
kwargs = args.value_kwargs
if args.value_model == 'none':
value_network = None
elif args.environment == 'LunarLander-v2':
assert "output_dim" not in kwargs and "final_activation" not in kwargs
value_network = MultilayerPerceptron(1,
final_activation='linear',
**args.value_kwargs)
batch = np.zeros((1, 8), dtype=np.float32)
value_network(batch) # build network
elif args.environment in ['CartPole-v0', 'CartPole-v1']:
assert "output_dim" not in kwargs and "final_activation" not in kwargs
value_network = MultilayerPerceptron(1,
final_activation='linear',
**args.value_kwargs)
batch = np.zeros((1, 4), dtype=np.float32)
value_network(batch) # build network
else:
if args.value_model == 'pairsleft':
value_network = PairsLeftBaseline(gam=args.gam)
else:
value_network = 'env'
if args.value_weights != "":
value_network.load_weights(args.value_weights)
return value_network
def make_value_network(args):
"""Return the value network for this run."""
kwargs = args.value_kwargs
if args.value_model == 'none':
value_network = None
elif args.environment == 'LunarLander-v2':
assert "output_dim" not in kwargs and "final_activation" not in kwargs
value_network = MultilayerPerceptron(1,
final_activation='linear',
**args.value_kwargs)
batch = np.zeros((1, 8), dtype=np.float32)
value_network(batch) # build network
elif args.environment in ['CartPole-v0', 'CartPole-v1']:
assert "output_dim" not in kwargs and "final_activation" not in kwargs
value_network = MultilayerPerceptron(1,
final_activation='linear',
**args.value_kwargs)
batch = np.zeros((1, 4), dtype=np.float32)
value_network(batch) # build network
elif args.value_model == 'pairsleft':
value_network = PairsLeftBaseline(gam=args.gam)
elif args.value_model == 'rnn':
value_network = RecurrentValueModel(**args.value_kwargs)
elif args.value_model == 'pool':
value_network = PoolingValueModel(**args.value_kwargs)
elif args.value_model == 'tvm':
value_network = TransformerValueModel(**args.value_kwargs)
batch = np.zeros(
(1, 10, 2 * args.k * int(args.distribution.split('-')[0])),
dtype=np.int32)
value_network(batch)
else:
value_network = 'env'
if args.value_weights != "":
value_network.load_weights(args.value_weights)
return value_network
def make_value_network(args):
"""Return the value network for this run."""
dims = {
'CartPole-v0': (4, 2),
'CartPole-v1': (4, 2),
'LunarLander-v2': (8, 4),
'RandomBinomialIdeal': (2 * args.variables * args.k, 1),
'MixedRandomBinomialIdeal': (2 * args.variables * args.k, 1),
'RandomPolynomialIdeal': (2 * args.variables * args.k, 1)
}[args.environment]
if args.environment in ['CartPole-v0', 'CartPole-v1', 'LunarLander-v2']:
if args.value_model == 'none':
value_network = None
else:
value_network = MultilayerPerceptron(dims[0],
args.value_hl,
1,
final_activation='linear')
else:
if args.value_model == 'none':
value_network = None
elif args.value_model == 'pairsleft':
value_network = PairsLeftBaseline(gam=args.gam)
elif args.value_model == 'degree':
value_network = AgentBaseline(BuchbergerAgent('degree'),
gam=args.gam)
elif args.value_model == 'agent':
agent = PPOAgent(
ParallelMultilayerPerceptron(dims[0], args.policy_hl))
agent.load_policy_weights(args.value_weights)
value_network = AgentBaseline(agent, gam=args.gam)
elif args.value_model == 'rnn' and args.value_weights != "":
value_network = ValueRNN(dims[0], args.value_hl[0])
value_network.load_weights(args.value_weights)
elif args.value_model == 'rnn':
value_network = ValueRNN(dims[0], args.value_hl[0])
return value_network
def make_policy_network(args):
"""Return the policy network for this run."""
dims = {
'CartPole-v0': (4, 2),
'CartPole-v1': (4, 2),
'LunarLander-v2': (8, 4),
'RandomBinomialIdeal': (2 * args.variables * args.k, 1),
'MixedRandomBinomialIdeal': (2 * args.variables * args.k, 1),
'RandomPolynomialIdeal': (2 * args.variables * args.k, 1)
}[args.environment]
if args.environment in [
'RandomBinomialIdeal', 'MixedRandomBinomialIdeal',
'RandomPolynomialIdeal'
]:
policy_network = ParallelMultilayerPerceptron(dims[0], args.policy_hl)
else:
policy_network = MultilayerPerceptron(dims[0], args.policy_hl, dims[1])
if args.policy_weights != "":
policy_network.load_weights(args.policy_weights)
return policy_network