(args: Args)
| 36 | |
| 37 | |
| 38 | def run_diffusion(args: Args): |
| 39 | |
| 40 | rng = jax.random.PRNGKey(seed=args.seed) |
| 41 | |
| 42 | ## setup env |
| 43 | |
| 44 | # recommended temperature for envs |
| 45 | temp_recommend = { |
| 46 | "ant": 0.1, |
| 47 | "halfcheetah": 0.4, |
| 48 | "hopper": 0.1, |
| 49 | "humanoidstandup": 0.1, |
| 50 | "humanoidrun": 0.1, |
| 51 | "walker2d": 0.1, |
| 52 | "pushT": 0.2, |
| 53 | } |
| 54 | Ndiffuse_recommend = { |
| 55 | "pushT": 200, |
| 56 | "humanoidrun": 300, |
| 57 | } |
| 58 | Nsample_recommend = { |
| 59 | "humanoidrun": 8192, |
| 60 | } |
| 61 | Hsample_recommend = { |
| 62 | "pushT": 40, |
| 63 | } |
| 64 | if not args.disable_recommended_params: |
| 65 | args.temp_sample = temp_recommend.get(args.env_name, args.temp_sample) |
| 66 | args.Ndiffuse = Ndiffuse_recommend.get(args.env_name, args.Ndiffuse) |
| 67 | args.Nsample = Nsample_recommend.get(args.env_name, args.Nsample) |
| 68 | args.Hsample = Hsample_recommend.get(args.env_name, args.Hsample) |
| 69 | print(f"override temp_sample to {args.temp_sample}") |
| 70 | env = mbd.envs.get_env(args.env_name) |
| 71 | Nx = env.observation_size |
| 72 | Nu = env.action_size |
| 73 | # env functions |
| 74 | step_env_jit = jax.jit(env.step) |
| 75 | reset_env_jit = jax.jit(env.reset) |
| 76 | # eval_us = jax.jit(functools.partial(mbd.utils.eval_us, step_env_jit)) |
| 77 | rollout_us = jax.jit(functools.partial(mbd.utils.rollout_us, step_env_jit)) |
| 78 | |
| 79 | rng, rng_reset = jax.random.split(rng) # NOTE: rng_reset should never be changed. |
| 80 | state_init = reset_env_jit(rng_reset) |
| 81 | |
| 82 | ## run diffusion |
| 83 | |
| 84 | betas = jnp.linspace(args.beta0, args.betaT, args.Ndiffuse) |
| 85 | alphas = 1.0 - betas |
| 86 | alphas_bar = jnp.cumprod(alphas) |
| 87 | sigmas = jnp.sqrt(1 - alphas_bar) |
| 88 | Sigmas_cond = ( |
| 89 | (1 - alphas) * (1 - jnp.sqrt(jnp.roll(alphas_bar, 1))) / (1 - alphas_bar) |
| 90 | ) |
| 91 | sigmas_cond = jnp.sqrt(Sigmas_cond) |
| 92 | sigmas_cond = sigmas_cond.at[0].set(0.0) |
| 93 | print(f"init sigma = {sigmas[-1]:.2e}") |
| 94 | |
| 95 | YN = jnp.zeros([args.Hsample, Nu]) |
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