Add sac training openmanipulator script

scripts/config/agent/open_manipulator_reacher_v0/sac.py

@@ -0,0 +1,112 @@
+# -*- coding: utf-8 -*-
+"""Run module for SAC on OpenManipulator.
+
+- Author: Curt Park
+- Contact: [email protected]
+"""
+
+import numpy as np
+import torch
+import torch.optim as optim
+
+from algorithms.common.networks.mlp import MLP, FlattenMLP, TanhGaussianDistParams
+from algorithms.sac.agent import Agent
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# hyper parameters
+hyper_params = {
+    "N_STEP": 3,
+    "GAMMA": 0.99,
+    "TAU": 1e-3,
+    "BUFFER_SIZE": int(1e6),
+    "BATCH_SIZE": 128,
+    "AUTO_ENTROPY_TUNING": True,
+    "LR_ACTOR": 3e-4,
+    "LR_VF": 3e-4,
+    "LR_QF1": 3e-4,
+    "LR_QF2": 3e-4,
+    "W_ENTROPY": 1e-3,
+    "W_MEAN_REG": 1e-3,
+    "W_STD_REG": 1e-3,
+    "W_PRE_ACTIVATION_REG": 0.0,
+    "LR_ENTROPY": 3e-4,
+    "DELAYED_UPDATE": 2,
+    "WEIGHT_DECAY": 0.0,
+    "INITIAL_RANDOM_ACTION": int(1e4),
+    "NETWORK": {
+        "ACTOR_HIDDEN_SIZES": [256, 256],
+        "VF_HIDDEN_SIZES": [256, 256],
+        "QF_HIDDEN_SIZES": [256, 256],
+    },
+}
+
+
+def get(env, args):
+    """Run training or test.
+
+    Args:
+        env (gym.Env): openAI Gym environment with continuous action space
+        args (argparse.Namespace): arguments including training settings
+
+    """
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+
+    hidden_sizes_actor = hyper_params["NETWORK"]["ACTOR_HIDDEN_SIZES"]
+    hidden_sizes_vf = hyper_params["NETWORK"]["VF_HIDDEN_SIZES"]
+    hidden_sizes_qf = hyper_params["NETWORK"]["QF_HIDDEN_SIZES"]
+
+    # target entropy
+    target_entropy = -np.prod((action_dim,)).item()  # heuristic
+
+    # create actor
+    actor = TanhGaussianDistParams(
+        input_size=state_dim, output_size=action_dim, hidden_sizes=hidden_sizes_actor
+    ).to(device)
+
+    # create v_critic
+    vf = MLP(input_size=state_dim, output_size=1, hidden_sizes=hidden_sizes_vf).to(
+        device
+    )
+    vf_target = MLP(
+        input_size=state_dim, output_size=1, hidden_sizes=hidden_sizes_vf
+    ).to(device)
+    vf_target.load_state_dict(vf.state_dict())
+
+    # create q_critic
+    qf_1 = FlattenMLP(
+        input_size=state_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes_qf
+    ).to(device)
+    qf_2 = FlattenMLP(
+        input_size=state_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes_qf
+    ).to(device)
+
+    # create optimizers
+    actor_optim = optim.Adam(
+        actor.parameters(),
+        lr=hyper_params["LR_ACTOR"],
+        weight_decay=hyper_params["WEIGHT_DECAY"],
+    )
+    vf_optim = optim.Adam(
+        vf.parameters(),
+        lr=hyper_params["LR_VF"],
+        weight_decay=hyper_params["WEIGHT_DECAY"],
+    )
+    qf_1_optim = optim.Adam(
+        qf_1.parameters(),
+        lr=hyper_params["LR_QF1"],
+        weight_decay=hyper_params["WEIGHT_DECAY"],
+    )
+    qf_2_optim = optim.Adam(
+        qf_2.parameters(),
+        lr=hyper_params["LR_QF2"],
+        weight_decay=hyper_params["WEIGHT_DECAY"],
+    )
+
+    # make tuples to create an agent
+    models = (actor, vf, vf_target, qf_1, qf_2)
+    optims = (actor_optim, vf_optim, qf_1_optim, qf_2_optim)
+
+    # create an agent
+    return Agent(env, args, hyper_params, models, optims, target_entropy)

scripts/config/agent/open_manipulator_reacher_v0/td3.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Run module for TD3 on LunarLanderContinuous-v2.
+"""Run module for TD3 on OpenManipulator.
 
 - Author: whikwon
 - Contact: [email protected]