added checkpoints and readme file

README.md

@@ -1,3 +1,26 @@
 ---
-license: apache-2.0
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+
 ---
+
+Find here pretrained model weights for the [Decision Transformer] (https://github.com/kzl/decision-transformer).
+Weights are available for 4 Atari games: Breakout, Pong, Qbert and Seaquest. Found in the checkpoints directory.
+We share models trained for one seed (123), whereas the paper contained weights for 3 random seeds.
+
+
+### Usage
+
+```
+conda env create -f conda_env.yml
+```
+
+Then, you can use the model like this:
+
+```python
+
+
+
+
+```

checkpoints/Breakout_123.pth

@@ -0,0 +1,3 @@
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+oid sha256:d56f3a83f8d5092b44f8fa812a51a210084854f542d89eb268be943729afbf75
+size 8329555

checkpoints/Qbert_123.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84683bb7fb5dae6d73c6b4cf97338af9a77dfb8c8c0d30b22f79b3358748eea4
+size 8971091

checkpoints/Seaquest_123.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2691ccaef484e97d22b2b47e87721d57c29747084fc11c7e15028a19ea0c7048
+size 8384339

conda_env.yml

@@ -0,0 +1,22 @@
+name: decision-transformer-atari
+channels:
+- pytorch
+dependencies:
+- python=3.7.9
+- pytorch
+- cudatoolkit=11.3
+- numpy
+- psutil
+- opencv
+- pip
+- pip:
+  - atari-py
+  - pyprind
+  - tensorflow-gpu>=1.13
+  - absl-py
+  - atari-py
+  - gin-config
+  - gym
+  - tqdm
+  - blosc
+  - git+https://github.com/google/dopamine.git

decision_transformer_atari.py

@@ -0,0 +1,396 @@
+"""
+The MIT License (MIT) Copyright (c) 2020 Andrej Karpathy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+"""
+
+"""
+GPT model:
+- the initial stem consists of a combination of token encoding and a positional encoding
+- the meat of it is a uniform sequence of Transformer blocks
+    - each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block
+    - all blocks feed into a central residual pathway similar to resnets
+- the final decoder is a linear projection into a vanilla Softmax classifier
+"""
+
+import math
+import logging
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+logger = logging.getLogger(__name__)
+
+import numpy as np
+
+
+class GELU(nn.Module):
+    def forward(self, input):
+        return F.gelu(input)
+
+
+class GPTConfig:
+    """ base GPT config, params common to all GPT versions """
+
+    embd_pdrop = 0.1
+    resid_pdrop = 0.1
+    attn_pdrop = 0.1
+
+    def __init__(self, vocab_size, block_size, **kwargs):
+        self.vocab_size = vocab_size
+        self.block_size = block_size
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+
+
+class GPT1Config(GPTConfig):
+    """ GPT-1 like network roughly 125M params """
+
+    n_layer = 12
+    n_head = 12
+    n_embd = 768
+
+
+class CausalSelfAttention(nn.Module):
+    """
+    A vanilla multi-head masked self-attention layer with a projection at the end.
+    It is possible to use torch.nn.MultiheadAttention here but I am including an
+    explicit implementation here to show that there is nothing too scary here.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads
+        self.key = nn.Linear(config.n_embd, config.n_embd)
+        self.query = nn.Linear(config.n_embd, config.n_embd)
+        self.value = nn.Linear(config.n_embd, config.n_embd)
+        # regularization
+        self.attn_drop = nn.Dropout(config.attn_pdrop)
+        self.resid_drop = nn.Dropout(config.resid_pdrop)
+        # output projection
+        self.proj = nn.Linear(config.n_embd, config.n_embd)
+        # causal mask to ensure that attention is only applied to the left in the input sequence
+        # self.register_buffer("mask", torch.tril(torch.ones(config.block_size, config.block_size))
+        #                              .view(1, 1, config.block_size, config.block_size))
+        self.register_buffer(
+            "mask",
+            torch.tril(torch.ones(config.block_size + 1, config.block_size + 1)).view(
+                1, 1, config.block_size + 1, config.block_size + 1
+            ),
+        )
+        self.n_head = config.n_head
+
+    def forward(self, x, layer_past=None):
+        B, T, C = x.size()
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        k = (
+            self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        )  # (B, nh, T, hs)
+        q = (
+            self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        )  # (B, nh, T, hs)
+        v = (
+            self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        )  # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float("-inf"))
+        att = F.softmax(att, dim=-1)
+        att = self.attn_drop(att)
+        y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = (
+            y.transpose(1, 2).contiguous().view(B, T, C)
+        )  # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_drop(self.proj(y))
+        return y
+
+
+class Block(nn.Module):
+    """ an unassuming Transformer block """
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.n_embd)
+        self.ln2 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.mlp = nn.Sequential(
+            nn.Linear(config.n_embd, 4 * config.n_embd),
+            GELU(),
+            nn.Linear(4 * config.n_embd, config.n_embd),
+            nn.Dropout(config.resid_pdrop),
+        )
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class GPT(nn.Module):
+    """  the full GPT language model, with a context size of block_size """
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+
+        self.model_type = config.model_type
+
+        # input embedding stem
+        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        # self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
+        self.pos_emb = nn.Parameter(
+            torch.zeros(1, config.block_size + 1, config.n_embd)
+        )
+        self.global_pos_emb = nn.Parameter(
+            torch.zeros(1, config.max_timestep + 1, config.n_embd)
+        )
+        self.drop = nn.Dropout(config.embd_pdrop)
+
+        # transformer
+        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
+        # decoder head
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        self.block_size = config.block_size
+        self.apply(self._init_weights)
+
+        logger.info(
+            "number of parameters: %e", sum(p.numel() for p in self.parameters())
+        )
+
+        self.state_encoder = nn.Sequential(
+            nn.Conv2d(4, 32, 8, stride=4, padding=0),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 4, stride=2, padding=0),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, stride=1, padding=0),
+            nn.ReLU(),
+            nn.Flatten(),
+            nn.Linear(3136, config.n_embd),
+            nn.Tanh(),
+        )
+
+        self.ret_emb = nn.Sequential(nn.Linear(1, config.n_embd), nn.Tanh())
+
+        self.action_embeddings = nn.Sequential(
+            nn.Embedding(config.vocab_size, config.n_embd), nn.Tanh()
+        )
+        nn.init.normal_(self.action_embeddings[0].weight, mean=0.0, std=0.02)
+
+    def get_block_size(self):
+        return self.block_size
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def configure_optimizers(self, train_config):
+        """
+        This long function is unfortunately doing something very simple and is being very defensive:
+        We are separating out all parameters of the model into two buckets: those that will experience
+        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
+        We are then returning the PyTorch optimizer object.
+        """
+
+        # separate out all parameters to those that will and won't experience regularizing weight decay
+        decay = set()
+        no_decay = set()
+        # whitelist_weight_modules = (torch.nn.Linear, )
+        whitelist_weight_modules = (torch.nn.Linear, torch.nn.Conv2d)
+        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
+        for mn, m in self.named_modules():
+            for pn, p in m.named_parameters():
+                fpn = "%s.%s" % (mn, pn) if mn else pn  # full param name
+
+                if pn.endswith("bias"):
+                    # all biases will not be decayed
+                    no_decay.add(fpn)
+                elif pn.endswith("weight") and isinstance(m, whitelist_weight_modules):
+                    # weights of whitelist modules will be weight decayed
+                    decay.add(fpn)
+                elif pn.endswith("weight") and isinstance(m, blacklist_weight_modules):
+                    # weights of blacklist modules will NOT be weight decayed
+                    no_decay.add(fpn)
+
+        # special case the position embedding parameter in the root GPT module as not decayed
+        no_decay.add("pos_emb")
+        no_decay.add("global_pos_emb")
+
+        # validate that we considered every parameter
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        inter_params = decay & no_decay
+        union_params = decay | no_decay
+        assert (
+            len(inter_params) == 0
+        ), "parameters %s made it into both decay/no_decay sets!" % (str(inter_params),)
+        assert len(param_dict.keys() - union_params) == 0, (
+            "parameters %s were not separated into either decay/no_decay set!"
+            % (str(param_dict.keys() - union_params),)
+        )
+
+        # create the pytorch optimizer object
+        optim_groups = [
+            {
+                "params": [param_dict[pn] for pn in sorted(list(decay))],
+                "weight_decay": train_config.weight_decay,
+            },
+            {
+                "params": [param_dict[pn] for pn in sorted(list(no_decay))],
+                "weight_decay": 0.0,
+            },
+        ]
+        optimizer = torch.optim.AdamW(
+            optim_groups, lr=train_config.learning_rate, betas=train_config.betas
+        )
+        return optimizer
+
+    # state, action, and return
+    def forward(self, states, actions, targets=None, rtgs=None, timesteps=None):
+        # states: (batch, block_size, 4*84*84)
+        # actions: (batch, block_size, 1)
+        # targets: (batch, block_size, 1)
+        # rtgs: (batch, block_size, 1)
+        # timesteps: (batch, 1, 1)
+
+        state_embeddings = self.state_encoder(
+            states.reshape(-1, 4, 84, 84).type(torch.float32).contiguous()
+        )  # (batch * block_size, n_embd)
+        state_embeddings = state_embeddings.reshape(
+            states.shape[0], states.shape[1], self.config.n_embd
+        )  # (batch, block_size, n_embd)
+
+        if actions is not None and self.model_type == "reward_conditioned":
+            rtg_embeddings = self.ret_emb(rtgs.type(torch.float32))
+            action_embeddings = self.action_embeddings(
+                actions.type(torch.long).squeeze(-1)
+            )  # (batch, block_size, n_embd)
+
+            token_embeddings = torch.zeros(
+                (
+                    states.shape[0],
+                    states.shape[1] * 3 - int(targets is None),
+                    self.config.n_embd,
+                ),
+                dtype=torch.float32,
+                device=state_embeddings.device,
+            )
+            token_embeddings[:, ::3, :] = rtg_embeddings
+            token_embeddings[:, 1::3, :] = state_embeddings
+            token_embeddings[:, 2::3, :] = action_embeddings[
+                :, -states.shape[1] + int(targets is None) :, :
+            ]
+        elif (
+            actions is None and self.model_type == "reward_conditioned"
+        ):  # only happens at very first timestep of evaluation
+            rtg_embeddings = self.ret_emb(rtgs.type(torch.float32))
+
+            token_embeddings = torch.zeros(
+                (states.shape[0], states.shape[1] * 2, self.config.n_embd),
+                dtype=torch.float32,
+                device=state_embeddings.device,
+            )
+            token_embeddings[:, ::2, :] = rtg_embeddings  # really just [:,0,:]
+            token_embeddings[:, 1::2, :] = state_embeddings  # really just [:,1,:]
+        elif actions is not None and self.model_type == "naive":
+            action_embeddings = self.action_embeddings(
+                actions.type(torch.long).squeeze(-1)
+            )  # (batch, block_size, n_embd)
+
+            token_embeddings = torch.zeros(
+                (
+                    states.shape[0],
+                    states.shape[1] * 2 - int(targets is None),
+                    self.config.n_embd,
+                ),
+                dtype=torch.float32,
+                device=state_embeddings.device,
+            )
+            token_embeddings[:, ::2, :] = state_embeddings
+            token_embeddings[:, 1::2, :] = action_embeddings[
+                :, -states.shape[1] + int(targets is None) :, :
+            ]
+        elif (
+            actions is None and self.model_type == "naive"
+        ):  # only happens at very first timestep of evaluation
+            token_embeddings = state_embeddings
+        else:
+            raise NotImplementedError()
+
+        batch_size = states.shape[0]
+        all_global_pos_emb = torch.repeat_interleave(
+            self.global_pos_emb, batch_size, dim=0
+        )  # batch_size, traj_length, n_embd
+
+        position_embeddings = (
+            torch.gather(
+                all_global_pos_emb,
+                1,
+                torch.repeat_interleave(timesteps, self.config.n_embd, dim=-1),
+            )
+            + self.pos_emb[:, : token_embeddings.shape[1], :]
+        )
+
+        x = self.drop(token_embeddings + position_embeddings)
+        x = self.blocks(x)
+        x = self.ln_f(x)
+        logits = self.head(x)
+
+        if actions is not None and self.model_type == "reward_conditioned":
+            logits = logits[:, 1::3, :]  # only keep predictions from state_embeddings
+        elif actions is None and self.model_type == "reward_conditioned":
+            logits = logits[:, 1:, :]
+        elif actions is not None and self.model_type == "naive":
+            logits = logits[:, ::2, :]  # only keep predictions from state_embeddings
+        elif actions is None and self.model_type == "naive":
+            logits = logits  # for completeness
+        else:
+            raise NotImplementedError()
+
+        # if we are given some desired targets also calculate the loss
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)), targets.reshape(-1)
+            )
+
+        return logits, loss
+
+
+if __name__ == "__main__":
+    vocab_size = 4
+    block_size = 90
+    model_type = "reward_conditioned"
+    timesteps = 2654
+
+    mconf = GPTConfig(
+        vocab_size,
+        block_size,
+        n_layer=6,
+        n_head=8,
+        n_embd=128,
+        model_type=model_type,
+        max_timestep=timesteps,
+    )
+    model = GPT(mconf)
+
+    checkpoint_path = "checkpoints/Breakout_123.pth"  # or Pong, Qbert, Seaquest
+    checkpoint = torch.load(checkpoint_path)
+    model.load_state_dict(checkpoint)
+