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jblitzar
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github-python

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if t % 500 == 0:
wandb.log({"Current Epsilon": cur_epsilon}, step=t * args.num_processes)
# Reset linear layer
if args.reset_interval > 0 and t % args.reset_interval == 0:
print(f"Resetting at step {t}")
for name, module in agent.Q.named_children():
for keyword in ["linear", "fc"]:
if keyword in name:
init_(module)
agent.Q_target = copy.deepcopy(agent.Q)
if t % 500 and not args.qrdqn or args.c51:
advantages = agent.advantage(state, epsilon(t))
mean_max_advantage = advantages.max(1)[0].mean()
mean_min_advantage = advantages.min(1)[0].mean()
wandb.log(
{
"Mean Max Advantage": mean_max_advantage,
"Mean Min Advantage": mean_min_advantage,
},
step=t * args.num_processes,
)
# Perform action and log results
next_state, reward, done, infos = envs.step(action)
masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done])
for i, info in enumerate(infos):
if "bad_transition" in info.keys():
print("Bad transition")
if level_sampler:
if expect_new_seed[i]:
level_seed = info["level_seed"]
level_seeds[i][0] = level_seed
if args.log_per_seed_stats:
new_episode(value, estimates, level_seed, i, step=t * args.num_processes)
expect_new_seed[i] = False
state_deque[i].append(state[i])
reward_deque[i].append(reward[i])
action_deque[i].append(action[i])
reward_stats_deque[i].append(reward[i])
if len(state_deque[i]) == args.multi_step or done[i]:
temp_reward = reward_deque[i]
# if args.reward_clip > 0:
# temp_reward = np.clip(temp_reward, -args.reward_clip, args.reward_clip)
n_reward = multi_step_reward(temp_reward, args.gamma)
n_state = state_deque[i][0]
n_action = action_deque[i][0]
replay_buffer.add(
n_state,
n_action,
next_state[i],
n_reward,
np.uint8(done[i]),
level_seeds[i],
)
if done[i]:
reward_deque_i = list(reward_deque[i])
for j in range(1, len(reward_deque_i)):
n_reward = multi_step_reward(reward_deque_i[j:], args.gamma)
n_state = state_deque[i][j]
n_action = action_deque[i][j]
replay_buffer.add(
n_state,
n_action,
next_state[i],
n_reward,
np.uint8(done[i]),
level_seeds[i],
)
expect_new_seed[i] = True
if "episode" in info.keys():
episode_reward = info["episode"]["r"]
ppo_normalised_reward = ppo_normalise_reward(episode_reward, args.env_name)
min_max_normalised_reward = min_max_normalise_reward(episode_reward, args.env_name)
wandb.log(
{
"Train Episode Returns": episode_reward,
"Train Episode Returns (normalised)": ppo_normalised_reward,
"Train Episode Returns (ppo normalised)": ppo_normalised_reward,
"Train Episode Returns (min-max normalised)": min_max_normalised_reward,
},
step=t * args.num_processes,
)
state_deque[i].clear()
reward_deque[i].clear()
action_deque[i].clear()
if args.log_per_seed_stats:
plot_level_returns(
level_seeds,
returns,
estimates,
gaps,
episode_reward,
i,
step=t * args.num_processes,
)
if args.autodrq: