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ddpg_move_to_a_point_action_max.py
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ddpg_move_to_a_point_action_max.py
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"""###IMP dummy_seed should always be last in the order in the OrderedDict below!!!
"""
import itertools
from ray import tune
from collections import OrderedDict
num_seeds = 10
var_env_configs = OrderedDict(
{
"state_space_dim": [2], # , 10, 12, 14] # [2**i for i in range(1,6)]
"action_space_dim": [2], # 2, 4, 8, 16] # [2**i for i in range(1,6)]
# "state_space_relevant_indices": [[0, 1]],
# "action_space_relevant_indices": [[0, 1]],
"delay": [0], # + [2**i for i in range(4)],
# 'sequence_length': [1], #, 2, 3, 4],#i for i in range(1,4)]
# 'reward_density': [0.25], # np.linspace(0.0, 1.0, num=5)
"make_denser": [True],
# 'terminal_state_density': [0.25], # np.linspace(0.1, 1.0, num=5)
"transition_noise": [0],
"reward_noise": [0], # , 1, 5, 10, 25] # Std dev. of normal dist.
"target_point": [[0, 0]],
"target_radius": [0.5],
"state_space_max": [10],
"action_space_max": [0.1, 0.25, 0.5, 1, 2, 4, 8],
"action_loss_weight": [0.0], # 0.01,
"time_unit": [1.0],
"transition_dynamics_order": [1],
"dummy_seed": [i for i in range(num_seeds)],
}
)
var_configs = OrderedDict({"env": var_env_configs})
env_config = {
"env": "RLToy-v0",
"horizon": 100,
"env_config": {
"seed": 0, # seed
"state_space_type": "continuous",
"action_space_type": "continuous",
"inertia": 1,
"reward_scale": 1.0,
"reward_function": "move_to_a_point",
# 'make_denser': True,
# "log_level": 'INFO',
"log_filename": "/tmp/ddpg_mv_pt.log",
},
}
algorithm = "DDPG"
agent_config = {
# Learning rate for the critic (Q-function) optimizer.
"critic_lr": 1e-3,
# Learning rate for the actor (policy) optimizer.
"actor_lr": 1e-3,
# Update the target by \tau * policy + (1-\tau) * target_policy
"tau": 0.02,
# How many steps of the model to sample before learning starts.
"learning_starts": 2000,
# Postprocess the policy network model output with these hidden layers. If
# use_state_preprocessor is False, then these will be the *only* hidden
# layers in the network.
"actor_hiddens": [32, 32],
# Postprocess the critic network model output with these hidden layers;
# again, if use_state_preprocessor is True, then the state will be
# preprocessed by the model specified with the "model" config option first.
"critic_hiddens": [32, 32],
# Apply a state preprocessor with spec given by the "model" config option
# (like other RL algorithms). This is mostly useful if you have a weird
# observation shape, like an image. Disabled by default.
"use_state_preprocessor": False,
# Hidden layers activation of the postprocessing stage of the policy
# network
"actor_hidden_activation": "relu",
# Hidden layers activation of the postprocessing state of the critic.
"critic_hidden_activation": "relu",
# N-step Q learning
"n_step": 1,
# Update the target network every `target_network_update_freq` steps.
"target_network_update_freq": 0,
# If True, use huber loss instead of squared loss for critic network
# Conventionally, no need to clip gradients if using a huber loss
"use_huber": False,
# Threshold of a huber loss
"huber_threshold": 1.0,
# Weights for L2 regularization
"l2_reg": 1e-6,
# If not None, clip gradients during optimization at this value
"grad_norm_clipping": None,
"buffer_size": 50000,
# If True prioritized replay buffer will be used.
"prioritized_replay": False,
# Alpha parameter for prioritized replay buffer.
"prioritized_replay_alpha": 0.6,
# Beta parameter for sampling from prioritized replay buffer.
"prioritized_replay_beta": 0.4,
# Time steps over which the beta parameter is annealed.
# "prioritized_replay_beta_annealing_timesteps": 20000,
# Final value of beta
"final_prioritized_replay_beta": 0.4,
# Epsilon to add to the TD errors when updating priorities.
"prioritized_replay_eps": 1e-6,
# "schedule_max_timesteps": 20000,
"timesteps_per_iteration": 1000,
# Update the replay buffer with this many samples at once. Note that this
# setting applies per-worker if num_workers > 1.
# "rollout_fragment_length": 1,
"rollout_fragment_length": 1, # Renamed from sample_batch_size in some Ray version
"train_batch_size": 32,
"min_iter_time_s": 0,
}
model_config = {
"model": {
# "fcnet_hiddens": [256, 256],
# "custom_preprocessor": "ohe",
"custom_options": {}, # extra options to pass to your preprocessor
# "no_final_linear": False,
# "vf_share_layers": True,
# "fcnet_activation": "tanh",
"use_lstm": False,
},
}
eval_config = {
"evaluation_interval": 1, # I think this means every x training_iterations
"evaluation_config": {
"explore": False,
"exploration_fraction": 0,
"exploration_final_eps": 0,
"evaluation_num_episodes": 10,
"horizon": 100,
"env_config": {
"dummy_eval": True, # hack Used to check if we are in evaluation mode or training mode inside Ray callback on_episode_end() to be able to write eval stats
"transition_noise": 0
if "state_space_type" in env_config["env_config"]
and env_config["env_config"]["state_space_type"] == "discrete"
else tune.function(lambda a: a.normal(0, 0)),
"reward_noise": tune.function(lambda a: a.normal(0, 0)),
"action_loss_weight": 0.0,
},
},
}
value_tuples = []
for config_type, config_dict in var_configs.items():
for key in config_dict:
assert isinstance(
var_configs[config_type][key], list
), "var_config should be a dict of dicts with lists as the leaf values to allow each configuration option to take multiple possible values"
value_tuples.append(var_configs[config_type][key])
cartesian_product_configs = list(itertools.product(*value_tuples))
print("Total number of configs. to run:", len(cartesian_product_configs))