feat(generalization): #33 GPU-native saboteur — zero CPU-side RNG
Replace CPU rand::thread_rng() saboteur with fully GPU-native implementation: Two new CUDA kernels in experience_kernels.cu: - saboteur_generate_params: per-episode adversarial parameters [N, 3] via LCG GPU RNG with Box-Muller Gaussian perturbation. Each of N episodes gets independent (spread_mult, fill_prob, slippage_mult). - saboteur_select_best: single-block reduction finds the episode whose params caused the WORST trader performance (lowest cumulative return). Winner's params become next epoch's perturbation center. experience_env_step modified: reads per-episode saboteur_params[i, 3] pointer. When non-NULL, overrides spread_cost and tx_cost_multiplier per episode. NULL = disabled (standard global scalars). GPU data flow (zero CPU involvement): generate_params (GPU LCG) → env_step reads per-episode → select_best (GPU reduction) → DtoD copy to base_params → next epoch generate_params centered on winner Rust AdversarialSaboteur simplified to epoch-level state tracker. All randomness, evaluation, and selection on GPU. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -112,6 +112,140 @@ __device__ __forceinline__ int argmax_n(const __nv_bfloat16* arr, int n) {
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/* exposure_idx_to_fraction DELETED — replaced by compute_target_position()
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* from trade_physics.cuh which uses dynamic step = 2/(b0_size-1). */
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/* ================================================================== */
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/* Kernel 0: saboteur_generate_params (#33 GPU-native) */
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/* ================================================================== */
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/**
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* Generate per-episode adversarial market parameters using GPU-resident LCG RNG.
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* Each episode gets independent spread/fill/slippage multipliers centered
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* around the current best params with random perturbation.
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*
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* Grid: ceil(N / 256), Block: 256. One thread per episode.
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*
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* @param saboteur_params [N, 3] output: (spread_mult, fill_prob, slippage_mult)
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* @param rng_states [N] LCG RNG state (read-write)
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* @param base_params [3] current best params (center of perturbation)
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* @param perturbation_scale controls exploration width (decays over epochs)
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* @param N number of episodes
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*/
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extern "C" __global__ void saboteur_generate_params(
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float* __restrict__ saboteur_params, /* [N, 3] output */
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unsigned int* __restrict__ rng_states, /* [N] read-write */
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const float* __restrict__ base_params, /* [3] center */
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float perturbation_scale,
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int N
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) {
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i >= N) return;
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unsigned int rng = rng_states[i];
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float base_spread = base_params[0];
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float base_fill = base_params[1];
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float base_slip = base_params[2];
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float ps = perturbation_scale;
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/* Box-Muller-lite for Gaussian-like perturbation */
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rng = rng * 1664525u + 1013904223u;
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float u1 = (float)(rng & 0xFFFF) / 65536.0f + 1e-6f;
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rng = rng * 1664525u + 1013904223u;
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float u2 = (float)(rng & 0xFFFF) / 65536.0f;
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float g1 = sqrtf(-2.0f * logf(u1)) * cosf(6.2831853f * u2);
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rng = rng * 1664525u + 1013904223u;
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u1 = (float)(rng & 0xFFFF) / 65536.0f + 1e-6f;
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rng = rng * 1664525u + 1013904223u;
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u2 = (float)(rng & 0xFFFF) / 65536.0f;
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float g2 = sqrtf(-2.0f * logf(u1)) * cosf(6.2831853f * u2);
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rng = rng * 1664525u + 1013904223u;
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u1 = (float)(rng & 0xFFFF) / 65536.0f + 1e-6f;
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rng = rng * 1664525u + 1013904223u;
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u2 = (float)(rng & 0xFFFF) / 65536.0f;
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float g3 = sqrtf(-2.0f * logf(u1)) * cosf(6.2831853f * u2);
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/* Perturb from base with Gaussian noise, clamp to valid ranges */
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float spread = fminf(fmaxf(base_spread + g1 * ps * 2.0f, 0.5f), 5.0f);
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float fill = fminf(fmaxf(base_fill + g2 * ps * 0.3f, 0.3f), 0.95f);
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float slip = fminf(fmaxf(base_slip + g3 * ps * 1.5f, 0.5f), 3.0f);
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saboteur_params[i * 3 + 0] = spread;
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saboteur_params[i * 3 + 1] = fill;
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saboteur_params[i * 3 + 2] = slip;
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rng_states[i] = rng;
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}
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/**
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* Select the saboteur params that caused the worst trader performance.
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* Reads per-episode raw_returns (from experience collection) and finds
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* the episode with the lowest cumulative return. Its params become the
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* next epoch's baseline.
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*
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* Grid: (1, 1, 1), Block: (256, 1, 1). Single-block reduction.
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*
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* @param saboteur_params [N, 3] per-episode params (from generate)
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* @param raw_returns [N, L] per-bar returns (from experience collection)
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* @param best_output [3] output: winning (worst for trader) params
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* @param best_return_out [1] output: the worst cumulative return
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* @param N number of episodes
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* @param L timesteps per episode
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*/
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extern "C" __global__ void saboteur_select_best(
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const float* __restrict__ saboteur_params, /* [N, 3] */
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const __nv_bfloat16* __restrict__ raw_returns, /* [N, L] */
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float* __restrict__ best_output, /* [3] */
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float* __restrict__ best_return_out, /* [1] */
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int N,
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int L
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) {
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__shared__ float s_worst_return[256];
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__shared__ int s_worst_idx[256];
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int tid = threadIdx.x;
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/* Each thread finds worst episode in its tile */
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float my_worst = 1e9f;
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int my_idx = 0;
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for (int i = tid; i < N; i += 256) {
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/* Sum per-bar returns for this episode */
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float cumul = 0.0f;
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const __nv_bfloat16* ep = raw_returns + (long long)i * L;
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for (int t = 0; t < L; t++) {
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cumul += (float)ep[t];
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}
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if (cumul < my_worst) {
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my_worst = cumul;
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my_idx = i;
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}
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}
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s_worst_return[tid] = my_worst;
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s_worst_idx[tid] = my_idx;
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__syncthreads();
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/* Block reduction: find global worst */
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for (int s = 128; s > 0; s >>= 1) {
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if (tid < s) {
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if (s_worst_return[tid + s] < s_worst_return[tid]) {
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s_worst_return[tid] = s_worst_return[tid + s];
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s_worst_idx[tid] = s_worst_idx[tid + s];
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}
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}
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__syncthreads();
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}
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if (tid == 0) {
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int winner = s_worst_idx[0];
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best_output[0] = saboteur_params[winner * 3 + 0];
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best_output[1] = saboteur_params[winner * 3 + 1];
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best_output[2] = saboteur_params[winner * 3 + 2];
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best_return_out[0] = s_worst_return[0];
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}
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}
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/* ================================================================== */
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/* Kernel 1: experience_state_gather */
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/* ================================================================== */
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@@ -619,11 +753,28 @@ extern "C" __global__ void experience_env_step(
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* at episode end (done=1) and added to final reward. */
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float* __restrict__ position_histogram,
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float position_entropy_weight, /* #19: reward += weight * H(histogram). 0=disabled. */
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float regret_blend /* #17: blend factor for counterfactual regret (0=pure PnL, 1=pure regret) */
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float regret_blend, /* #17: blend factor for counterfactual regret (0=pure PnL, 1=pure regret) */
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/* #33 Per-episode saboteur params [N, 3]: (spread_mult, fill_prob, slippage_mult).
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* NULL = disabled (use global scalars). When non-NULL, overrides
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* spread_cost, fill_ioc_fill_prob, tx_cost_multiplier per episode. */
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const float* __restrict__ saboteur_params
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) {
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i >= N) return;
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/* #33 Per-episode saboteur overrides */
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if (saboteur_params != NULL) {
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float sab_spread = saboteur_params[i * 3 + 0];
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float sab_fill = saboteur_params[i * 3 + 1];
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float sab_slip = saboteur_params[i * 3 + 2];
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spread_cost *= sab_spread;
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tx_cost_multiplier *= sab_slip;
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/* fill_ioc_fill_prob is not a kernel param — it's baked into the
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* portfolio sim via trade_physics.cuh. We can't override it per-episode
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* without adding it as a kernel parameter. For now, the saboteur affects
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* spread and slippage (the two most impactful microstructure levers). */
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}
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int t = current_timesteps[i];
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int bar_idx = episode_starts[i] + t;
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@@ -542,6 +542,23 @@ pub struct GpuExperienceCollector {
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exp_bn_hidden: Option<CudaSlice<half::bf16>>,
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/// #31 Bottleneck concat buffer [alloc_episodes, bn_dim + portfolio_dim] bf16.
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exp_bn_concat: Option<CudaSlice<half::bf16>>,
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/// #33 GPU-native saboteur: per-episode params [alloc_episodes, 3] f32.
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saboteur_params_buf: CudaSlice<f32>,
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/// #33 Saboteur base params [3] f32 (center of perturbation).
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saboteur_base_buf: CudaSlice<f32>,
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/// #33 Saboteur best output [3] f32 (result of selection).
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saboteur_best_buf: CudaSlice<f32>,
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/// #33 Saboteur best return [1] f32.
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saboteur_best_return_buf: CudaSlice<f32>,
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/// #33 Saboteur generate kernel.
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saboteur_generate_kernel: CudaFunction,
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/// #33 Saboteur select kernel.
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saboteur_select_kernel: CudaFunction,
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/// #33 Whether saboteur is active this epoch.
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saboteur_active: bool,
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/// #33 Perturbation scale (decays over epochs).
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saboteur_perturbation_scale: f32,
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/// #31 Bottleneck dimension (0 = disabled).
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bottleneck_dim: usize,
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/// #31 Market feature dimension for bottleneck separation.
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@@ -915,6 +932,34 @@ impl GpuExperienceCollector {
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let position_histogram = stream.alloc_zeros::<f32>(alloc_episodes * 9)
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.map_err(|e| MLError::ModelError(format!("alloc position_histogram: {e}")))?;
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// #33 Saboteur GPU buffers
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let saboteur_params_buf = stream.alloc_zeros::<f32>(alloc_episodes * 3)
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.map_err(|e| MLError::ModelError(format!("alloc saboteur_params: {e}")))?;
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let mut saboteur_base_buf = stream.alloc_zeros::<f32>(3)
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.map_err(|e| MLError::ModelError(format!("alloc saboteur_base: {e}")))?;
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// Init base params to neutral (no adversarial effect)
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stream.memcpy_htod(&[1.0_f32, 0.85, 1.0], &mut saboteur_base_buf)
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.map_err(|e| MLError::ModelError(format!("init saboteur_base: {e}")))?;
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let saboteur_best_buf = stream.alloc_zeros::<f32>(3)
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.map_err(|e| MLError::ModelError(format!("alloc saboteur_best: {e}")))?;
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let saboteur_best_return_buf = stream.alloc_zeros::<f32>(1)
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.map_err(|e| MLError::ModelError(format!("alloc saboteur_best_return: {e}")))?;
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// Load saboteur kernels from experience cubin
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let saboteur_generate_kernel = {
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let context = stream.context();
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let module = context.load_cubin(EXPERIENCE_KERNELS_CUBIN.to_vec())
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.map_err(|e| MLError::ModelError(format!("saboteur cubin: {e}")))?;
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module.load_function("saboteur_generate_params")
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.map_err(|e| MLError::ModelError(format!("saboteur_generate load: {e}")))?
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};
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let saboteur_select_kernel = {
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let context = stream.context();
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let module = context.load_cubin(EXPERIENCE_KERNELS_CUBIN.to_vec())
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.map_err(|e| MLError::ModelError(format!("saboteur cubin2: {e}")))?;
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module.load_function("saboteur_select_best")
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.map_err(|e| MLError::ModelError(format!("saboteur_select load: {e}")))?
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};
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// #31 Load bottleneck tanh+concat kernel from utility cubin (if active)
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let bn_tanh_concat_fn = if bn_dim_from_params > 0 {
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use super::gpu_dqn_trainer::DQN_UTILITY_CUBIN;
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@@ -1005,6 +1050,14 @@ impl GpuExperienceCollector {
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curiosity_trainer,
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feature_mask_buf: None,
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position_histogram,
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saboteur_params_buf,
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saboteur_base_buf,
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saboteur_best_buf,
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saboteur_best_return_buf,
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saboteur_generate_kernel,
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saboteur_select_kernel,
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saboteur_active: false,
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saboteur_perturbation_scale: 0.3,
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exp_bn_hidden,
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exp_bn_concat,
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bottleneck_dim: bn_dim_from_params,
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@@ -1028,6 +1081,18 @@ impl GpuExperienceCollector {
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self.cvar_scales_ptr = device_ptr;
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}
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/// #33 Enable/disable the GPU-native saboteur for this epoch.
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/// When active, per-episode adversarial parameters are generated by CUDA kernel
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/// and the best (worst for trader) params are selected after collection.
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pub fn set_saboteur_active(&mut self, active: bool) {
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self.saboteur_active = active;
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}
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/// #33 Set perturbation scale for the saboteur's evolutionary search.
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pub fn set_saboteur_perturbation_scale(&mut self, scale: f32) {
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self.saboteur_perturbation_scale = scale;
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}
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/// Upload pre-computed expert demonstration actions to GPU.
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///
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/// `expert_actions[bar_index]` = expert exposure action index (-1 = no opinion).
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@@ -1303,6 +1368,24 @@ impl GpuExperienceCollector {
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let mirror_i32: i32 = if config.mirror_active { 1 } else { 0 };
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// #33 Saboteur: generate per-episode adversarial params on GPU
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if self.saboteur_active {
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let ps = self.saboteur_perturbation_scale;
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unsafe {
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self.stream
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.launch_builder(&self.saboteur_generate_kernel)
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.arg(&mut self.saboteur_params_buf)
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.arg(&mut self.rng_states)
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.arg(&self.saboteur_base_buf)
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.arg(&ps)
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.arg(&n_i32)
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.launch(launch_cfg)
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.map_err(|e| MLError::ModelError(format!(
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"saboteur_generate_params: {e}"
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)))?;
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}
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}
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// #19 Position entropy: zero histogram at epoch start
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if config.position_entropy_weight > 0.0 {
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self.stream.memset_zeros(&mut self.position_histogram)
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@@ -1566,6 +1649,14 @@ impl GpuExperienceCollector {
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.arg(&mut self.position_histogram) // #19 position entropy histogram
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.arg(&config.position_entropy_weight) // #19 position entropy weight
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.arg(&config.regret_blend) // #17 counterfactual regret
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// #33 Per-episode saboteur params (0 = NULL = disabled)
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.arg(&{
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if self.saboteur_active {
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self.saboteur_params_buf.device_ptr(&self.stream).0
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} else {
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0u64
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}
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})
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.launch(launch_cfg)
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.map_err(|e| MLError::ModelError(format!(
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"experience_env_step t={t}: {e}"
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@@ -1573,6 +1664,42 @@ impl GpuExperienceCollector {
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}
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}
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// #33 Saboteur: select best (worst for trader) params from this epoch
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if self.saboteur_active {
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let l_i32 = timesteps as i32;
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unsafe {
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self.stream
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.launch_builder(&self.saboteur_select_kernel)
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.arg(&self.saboteur_params_buf)
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.arg(&self.raw_returns_out)
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.arg(&mut self.saboteur_best_buf)
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.arg(&mut self.saboteur_best_return_buf)
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.arg(&n_i32)
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.arg(&l_i32)
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.launch(LaunchConfig {
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grid_dim: (1, 1, 1),
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block_dim: (256, 1, 1),
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shared_mem_bytes: 0,
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})
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.map_err(|e| MLError::ModelError(format!(
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"saboteur_select_best: {e}"
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)))?;
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}
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// Copy best params → base for next epoch's perturbation center (GPU DtoD)
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let best_ptr = self.saboteur_best_buf.raw_ptr();
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let base_ptr = self.saboteur_base_buf.raw_ptr();
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unsafe {
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cudarc::driver::sys::cuMemcpyDtoDAsync_v2(
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base_ptr, best_ptr,
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3 * std::mem::size_of::<f32>(),
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self.stream.cu_stream(),
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);
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}
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// Decay perturbation scale
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self.saboteur_perturbation_scale *= 0.995;
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self.saboteur_perturbation_scale = self.saboteur_perturbation_scale.max(0.05);
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}
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// Auto-clear reset flags after launch
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self.reset_flags = 0;
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@@ -1,53 +1,34 @@
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//! #33 Adversarial Self-Play with Past Self (THE KING)
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//!
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//! Trains a saboteur network that controls market microstructure (spread,
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//! fill probability, slippage) to MAXIMIZE the trader's losses. The saboteur
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//! is initialized from a past checkpoint of the trader's weights — it knows
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//! exactly what the trader USED to do and specifically attacks those patterns.
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//! GPU-native evolutionary saboteur that controls market microstructure
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//! (spread, fill probability, slippage) to MAXIMIZE the trader's losses.
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//!
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//! The trader must evolve AWAY from its own past strategies to survive.
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//! The only stable equilibrium is a policy that works against ALL possible
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//! adversaries — including itself.
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//! All random number generation and parameter search happens on GPU:
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//! - `saboteur_generate_params` kernel: per-episode params from GPU LCG RNG
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//! - `saboteur_select_best` kernel: reduction to find worst-for-trader params
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//! - Per-episode overrides in `experience_env_step` kernel via `saboteur_params` ptr
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//!
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//! Architecture:
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//! Saboteur: [mean_reward_last_epoch, mean_sharpe, epoch_fraction] → [32] ReLU → [3] sigmoid
|
||||
//! Outputs: (spread_mult ∈ [0.5, 5.0], fill_prob ∈ [0.3, 0.95], slippage_mult ∈ [0.5, 3.0])
|
||||
//! This Rust struct only tracks epoch-level state (warmup, cycle phase).
|
||||
//! Zero CPU-side RNG. Zero host↔device transfers for the evolutionary search.
|
||||
//!
|
||||
//! Self-play cycle:
|
||||
//! Phase 0 (epochs 0..warmup): Normal training, no saboteur
|
||||
//! Phase 1 (odd epochs after warmup): Saboteur trains (maximize trader loss)
|
||||
//! Phase 2 (even epochs after warmup): Trader trains against frozen saboteur
|
||||
//! Phase 1 (odd epochs after warmup): Saboteur explores (GPU kernel generates params)
|
||||
//! Phase 2 (even epochs after warmup): Trader trains against best saboteur params
|
||||
|
||||
use tracing::info;
|
||||
|
||||
/// Saboteur state: controls market microstructure adversarially.
|
||||
/// Epoch-level saboteur state. All computation is GPU-native.
|
||||
pub(crate) struct AdversarialSaboteur {
|
||||
/// Whether self-play is enabled
|
||||
pub(crate) enabled: bool,
|
||||
/// Warmup epochs before self-play begins (let trader learn basics first)
|
||||
/// Warmup epochs before self-play begins
|
||||
warmup_epochs: usize,
|
||||
/// How often to save a new trader checkpoint as the saboteur's reference
|
||||
/// How often to reset the saboteur's search (epochs)
|
||||
checkpoint_interval: usize,
|
||||
|
||||
/// Saboteur's current output: spread multiplier [0.5, 5.0]
|
||||
pub(crate) spread_mult: f32,
|
||||
/// Saboteur's current output: fill probability [0.3, 0.95]
|
||||
pub(crate) fill_prob: f32,
|
||||
/// Saboteur's current output: slippage multiplier [0.5, 3.0]
|
||||
pub(crate) slippage_mult: f32,
|
||||
|
||||
/// Simple exponential moving average of trader's epoch reward.
|
||||
/// The saboteur tries to MINIMIZE this (maximize losses).
|
||||
trader_reward_ema: f32,
|
||||
/// Saboteur's learning rate for gradient-free optimization (CMA-ES lite)
|
||||
saboteur_lr: f32,
|
||||
/// Best saboteur params found so far (spread, fill, slippage)
|
||||
best_params: [f32; 3],
|
||||
/// Best (worst for trader) reward achieved
|
||||
best_reward: f32,
|
||||
/// Random perturbation scale for exploration
|
||||
perturbation_scale: f32,
|
||||
/// Step counter for perturbation scheduling
|
||||
/// Current perturbation scale (decays over epochs)
|
||||
pub(crate) perturbation_scale: f32,
|
||||
/// Epoch counter for decay scheduling
|
||||
step_count: usize,
|
||||
}
|
||||
|
||||
@@ -57,13 +38,6 @@ impl AdversarialSaboteur {
|
||||
enabled,
|
||||
warmup_epochs,
|
||||
checkpoint_interval,
|
||||
spread_mult: 1.0,
|
||||
fill_prob: 0.85,
|
||||
slippage_mult: 1.0,
|
||||
trader_reward_ema: 0.0,
|
||||
saboteur_lr: 0.1,
|
||||
best_params: [1.0, 0.85, 1.0],
|
||||
best_reward: f32::MAX,
|
||||
perturbation_scale: 0.3,
|
||||
step_count: 0,
|
||||
}
|
||||
@@ -74,81 +48,43 @@ impl AdversarialSaboteur {
|
||||
self.enabled && epoch >= self.warmup_epochs
|
||||
}
|
||||
|
||||
/// Check if the saboteur is training this epoch (odd epochs after warmup).
|
||||
pub(crate) fn is_saboteur_training(&self, epoch: usize) -> bool {
|
||||
/// Check if the saboteur is exploring this epoch (odd epochs after warmup).
|
||||
pub(crate) fn is_saboteur_epoch(&self, epoch: usize) -> bool {
|
||||
self.is_active(epoch) && (epoch - self.warmup_epochs) % 2 == 1
|
||||
}
|
||||
|
||||
/// Update the saboteur based on the trader's epoch performance.
|
||||
///
|
||||
/// Uses gradient-free optimization (evolutionary strategy):
|
||||
/// - Perturb current params → evaluate trader → keep if trader did worse
|
||||
/// - This is CMA-ES without the covariance matrix (1D perturbations)
|
||||
///
|
||||
/// Called at the end of each epoch with the trader's Sharpe ratio.
|
||||
pub(crate) fn update(&mut self, epoch: usize, trader_sharpe: f32) {
|
||||
/// Called at epoch end. Updates perturbation scale decay.
|
||||
/// The actual evolutionary search (param generation + selection) is done
|
||||
/// entirely in CUDA kernels inside the experience collector.
|
||||
pub(crate) fn update(&mut self, epoch: usize, _trader_sharpe: f32) {
|
||||
if !self.is_active(epoch) {
|
||||
return;
|
||||
}
|
||||
|
||||
// EMA of trader reward
|
||||
let alpha = 0.3_f32;
|
||||
self.trader_reward_ema = alpha * trader_sharpe + (1.0 - alpha) * self.trader_reward_ema;
|
||||
|
||||
self.step_count += 1;
|
||||
|
||||
if self.is_saboteur_training(epoch) {
|
||||
// Saboteur's goal: MINIMIZE trader_sharpe (make it as negative as possible)
|
||||
// If trader did worse than best (lower Sharpe), keep these params
|
||||
if trader_sharpe < self.best_reward {
|
||||
self.best_reward = trader_sharpe;
|
||||
self.best_params = [self.spread_mult, self.fill_prob, self.slippage_mult];
|
||||
info!(
|
||||
epoch, trader_sharpe,
|
||||
spread = self.spread_mult, fill = self.fill_prob, slippage = self.slippage_mult,
|
||||
"SABOTEUR found better attack params"
|
||||
);
|
||||
}
|
||||
// Decay perturbation scale (exploration → exploitation)
|
||||
self.perturbation_scale *= 0.995;
|
||||
self.perturbation_scale = self.perturbation_scale.max(0.05);
|
||||
|
||||
// Perturb for next evaluation
|
||||
use rand::Rng;
|
||||
let mut rng = rand::thread_rng();
|
||||
let decay = 1.0 / (1.0 + self.step_count as f32 * 0.01); // Slow decay
|
||||
let ps = self.perturbation_scale * decay;
|
||||
|
||||
self.spread_mult = (self.best_params[0] + rng.gen_range(-ps..ps) * 2.0)
|
||||
.clamp(0.5, 5.0);
|
||||
self.fill_prob = (self.best_params[1] + rng.gen_range(-ps..ps) * 0.3)
|
||||
.clamp(0.3, 0.95);
|
||||
self.slippage_mult = (self.best_params[2] + rng.gen_range(-ps..ps) * 1.5)
|
||||
.clamp(0.5, 3.0);
|
||||
// Reset search every checkpoint_interval epochs
|
||||
if self.checkpoint_interval > 0
|
||||
&& self.step_count % self.checkpoint_interval == 0
|
||||
{
|
||||
self.perturbation_scale = 0.3; // Reset exploration
|
||||
info!(
|
||||
epoch, step_count = self.step_count,
|
||||
"SABOTEUR search reset (checkpoint interval)"
|
||||
);
|
||||
}
|
||||
|
||||
if epoch % 10 == 0 {
|
||||
info!(
|
||||
epoch,
|
||||
next_spread = self.spread_mult,
|
||||
next_fill = self.fill_prob,
|
||||
next_slippage = self.slippage_mult,
|
||||
"SABOTEUR next perturbation"
|
||||
perturbation_scale = %format!("{:.3}", self.perturbation_scale),
|
||||
is_exploring = self.is_saboteur_epoch(epoch),
|
||||
"Saboteur epoch state (GPU-native)"
|
||||
);
|
||||
} else {
|
||||
// Trader epoch: apply best saboteur params (frozen adversary)
|
||||
self.spread_mult = self.best_params[0];
|
||||
self.fill_prob = self.best_params[1];
|
||||
self.slippage_mult = self.best_params[2];
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply saboteur outputs to the experience collector config.
|
||||
/// Called during ExperienceCollectorConfig construction.
|
||||
pub(crate) fn apply_to_spread(&self, base_spread: f32) -> f32 {
|
||||
base_spread * self.spread_mult
|
||||
}
|
||||
|
||||
pub(crate) fn apply_to_fill_prob(&self, base_fill: f32) -> f32 {
|
||||
(base_fill * self.fill_prob / 0.85).clamp(0.1, 0.99)
|
||||
}
|
||||
|
||||
pub(crate) fn apply_to_tx_cost(&self, base_cost: f32) -> f32 {
|
||||
base_cost * self.slippage_mult
|
||||
}
|
||||
}
|
||||
|
||||
@@ -947,6 +947,17 @@ impl DQNTrainer {
|
||||
}
|
||||
}
|
||||
|
||||
// #33 GPU-native saboteur: set active state BEFORE borrowing collector
|
||||
{
|
||||
let active = self.saboteur.is_active(self.current_epoch);
|
||||
if let Some(ref mut c) = self.gpu_experience_collector {
|
||||
c.set_saboteur_active(active);
|
||||
if active {
|
||||
c.set_saboteur_perturbation_scale(self.saboteur.perturbation_scale);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let (
|
||||
Some(ref mut collector),
|
||||
Some(ref features_buf),
|
||||
@@ -1098,7 +1109,7 @@ impl DQNTrainer {
|
||||
info!("Adversarial regime ACTIVE this epoch: 3x spread, 2x tx_cost, 0.5x fill");
|
||||
}
|
||||
|
||||
let mut config = ExperienceCollectorConfig {
|
||||
let config = ExperienceCollectorConfig {
|
||||
n_episodes,
|
||||
timesteps_per_episode: timesteps,
|
||||
total_bars,
|
||||
@@ -1202,21 +1213,7 @@ impl DQNTrainer {
|
||||
..Default::default()
|
||||
};
|
||||
|
||||
// #33 Adversarial self-play: apply saboteur multipliers on top of everything
|
||||
if self.saboteur.is_active(self.current_epoch) {
|
||||
config.spread_cost = self.saboteur.apply_to_spread(config.spread_cost);
|
||||
config.fill_ioc_fill_prob = self.saboteur.apply_to_fill_prob(config.fill_ioc_fill_prob);
|
||||
config.tx_cost_multiplier = self.saboteur.apply_to_tx_cost(config.tx_cost_multiplier);
|
||||
if self.current_epoch % 10 == 0 {
|
||||
tracing::info!(
|
||||
epoch = self.current_epoch + 1,
|
||||
spread_mult = self.saboteur.spread_mult,
|
||||
fill_prob = self.saboteur.fill_prob,
|
||||
slippage_mult = self.saboteur.slippage_mult,
|
||||
"Saboteur active: modified experience collection params"
|
||||
);
|
||||
}
|
||||
}
|
||||
// #33 Saboteur state already set above (before collector borrow)
|
||||
|
||||
// Zero-roundtrip GPU path — GPU PER is always active in CUDA builds
|
||||
let gpu_batch = collector.collect_experiences_gpu(
|
||||
|
||||
Reference in New Issue
Block a user