SimingSiming/a2c-AntBulletEnv-v0

A2C Agent playing AntBulletEnv-v0

This is a trained model of a A2C agent playing AntBulletEnv-v0 using the stable-baselines3 library.

Usage (with Stable-baselines3)

parameters

model = A2C(policy = "MlpPolicy",
            env = env,
            gae_lambda = 0.9,
            gamma = 0.99,
            learning_rate = 0.00096,
            max_grad_norm = 0.5,
            n_steps = 8,
            vf_coef = 0.4,
            ent_coef = 0.0,
            tensorboard_log = "./tensorboard",
            policy_kwargs=dict(
            log_std_init=-2, ortho_init=False),
            normalize_advantage=False,
            use_rms_prop= True,
            use_sde= True,
            verbose=1)
...